JP5732201B2 - Classification model generation device, cell classification device, incubator, cell culture method and program - Google Patents

Description

  The present invention relates to a classification model generation device, a cell classification device, an incubator, a cell culture method, and a program.

  Currently, techniques for staining cells are used in detection of spontaneous cancer in cancer research, detection of heterogeneous cells other than therapeutic cells in regenerative medicine, clinical research of mesenchymal stem cells (MSC), and the like. This is because cell types, states, and the like can be classified visually only by sensory means without staining the cells. A technique for staining cells expressing a transcription factor has been disclosed (see Patent Document 1). Further, a technique for extracting a specific cell group from all cells in a captured image using both images obtained by binarizing a bright field image of a cell and a fluorescence image of the cell is disclosed (Patent Document 2). reference).

JP 2007-195533 A JP-A-2-12060

  However, there is a problem that cells are destroyed by staining. Furthermore, there is a problem that the staining reagent is expensive and that the cell staining itself is troublesome. An object of the present invention is to provide a technique for confirming the type, state, etc. of a cell without destroying the cell and inexpensively and easily.

In order to solve the above problem, a classification model generation device according to one aspect of the present invention is an unknown attribute imaging image that is an imaging image of an unknown adhesion-type cell that adheres to a culture vessel, or the unknown attribute imaging. A classification model generation device for generating a classification model for classifying adhesive cells in an image for each attribute based on morphological characteristics of the adhesive cells, and imaging a plurality of adhesive cells having the same attribute A cell object recognizing unit for recognizing an imaging region of each adhesion-related cell as an individual cell object from a known identical attribute captured image that is a captured image, and the cell having a predetermined morphological feature amount from the cell object A characteristic object extracting unit for extracting an object as a characteristic object, a morphological characteristic amount of the characteristic object, and the characteristic object; A candidate classification model forming unit that forms a candidate classification model that is a candidate for the classification model using a plurality of attributes of the adhesion-based cells recognized in the above, and the candidate classification model forming unit according to the extracted characteristic object A classification model determining unit that determines the classification model from among the plurality of candidate classification models formed by the candidate classification model forming unit , wherein the candidate classification model forming unit uses adhesive cells as morphological feature quantities of the characteristic objects. The value indicating the maximum value of the crossing lines, the value indicating the length when the adhesion type cell is assumed to be pseudo linear, the value indicating the width when the adhesion type cell is assumed to be pseudo linear Using at least one of the morphological features, and using a plurality of attributes including name, culture condition, and activity as the plurality of attributes of the adherent cell recognized as the characteristic object And forming an auxiliary classification model.

In order to solve the above-described problem, a cell classification device according to another aspect of the present invention includes an unknown attribute captured image obtained by capturing an image of an unknown adherent cell that adheres to a culture vessel, or the unknown attribute. A classification model storage unit that stores a classification model for classifying adhesive cells in a captured image for each attribute based on morphological characteristics of the adhesive cells, and imaging individual adhesive cells from the unknown attribute captured image A cell object recognition unit for recognizing a region as an individual cell object, and a morphological feature amount of the cell object recognized from the unknown attribute captured image by the cell object recognition unit or a feature amount calculated from the morphological feature amount and by charging to the classification model, and a cell classification unit which classifies the adhesive system cells of the unknown attributes captured image or the unknown attribute the captured image, wherein A cell object storage unit for storing the cell object recognized from a known identical-attribute captured image, which is a captured image obtained by imaging a plurality of adhesion-type cells with the same attribute known by a cell object recognition unit, and the cell classification unit includes the cell classification unit Characterizing the cell object having a predetermined morphological feature amount from among the cell objects stored in the cell object storage unit when classifying the unknown attribute captured image or the adherent cells in the unknown attribute captured image A candidate classification model that is a candidate for the classification model is extracted by using a morphological feature amount of the characteristic object and a plurality of attributes of the adhesive cells recognized as the characteristic object. Among the plurality of candidate classification models generated according to the characteristic object, And a classification model generating unit for determining a class model, the classification model forming portion, a morphological characteristic of the characteristic objects, the value indicating the maximum value among the line across the adhesive system cells, the adhesive system cells Using at least one morphological feature value of a value indicating a length when assumed to be pseudo-linear, a value indicating a width when assuming that the adherent cell is pseudo-linear, and The candidate classification model is formed using a plurality of attributes including a name, a culture condition, and an activity as a plurality of attributes of the adherent cell recognized as the characteristic object.

In order to solve the above problem, an incubator according to another aspect of the present invention contains a culture vessel for culturing adhesive cells, and can maintain the interior at a predetermined environmental condition, in an imaging device for capturing an image of the adhesive system cells contained in the culture vessel, the Louis Nkyubeta a said cell classification device, said culture vessel based on the classification result of the adhesive system cells using the classification model It further comprises a culture condition changing unit that changes the culture conditions of the adhesive cells in the inside.

In order to solve the above problem, a culture method according to another embodiment of the present invention is characterized by using the above incubator.

In order to solve the above-described problem, a program according to another aspect of the present invention is an unknown attribute captured image that is an captured image in which an unknown adhesive cell that adheres to a culture vessel is captured, or the unknown attribute captured image A plurality of adherent cells with the same attribute are imaged by a computer of a classification model generating device that generates a classification model for classifying the adhesive cells in each of the attributes based on the morphological characteristics of the adhesive cells. A cell object recognition step for recognizing an imaging region of each adhesion-related cell as an individual cell object from a known identical-attribute imaging image that is a captured image, and the cell object having a predetermined morphological feature amount from the cell object A characteristic object extracting step of extracting the characteristic object as a characteristic object, a morphological characteristic amount of the characteristic object, and the characteristic A candidate classification model forming step for forming a candidate classification model that is a candidate for the classification model using a plurality of attributes of the adhesion-type cells recognized as objects, and formation of the candidate classification model according to the extracted characteristic object A classification model determination step for determining the classification model from among the plurality of candidate classification models formed in the step, wherein the candidate classification model formation step includes a morphological feature of the characteristic object As a quantity, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, and a case where the adherent cells are assumed to be pseudo linear The adhesive system recognized as the characteristic object using at least one morphological characteristic amount of the value indicating the width of As a plurality of attributes of cells, names, and forming the candidate classification model using culture conditions, a plurality of attributes including activity.

In order to solve the above-described problem, a program according to another aspect of the present invention is an unknown attribute captured image that is an captured image in which an unknown adhesive cell that adheres to a culture vessel is captured, or the unknown attribute captured image In the computer of the cell classification device that stores a classification model for classifying the adhesion-type cells in each attribute based on the morphological characteristics of the adhesion-type cells, the imaging area of each adhesion-type cell from the unknown attribute imaging image A cell object recognition step for recognizing a cell object as an individual cell object, and a morphological feature amount of the cell object recognized from the captured image of unknown attributes by the cell object recognition step or a feature amount calculated from the morphological feature amount. , Classifying the unknown attribute captured image or the adherent cells in the unknown attribute captured image by entering the classification model A cell sorting step, the cell object storage step of storing the recognized said cell object from the known identity attributes captured image is a plurality of adhesive systems captured image cell is imaged in a known identical attribute by the cell object recognition step, A predetermined morphological feature amount among the cell objects stored in the cell object storing step when the unknown attribute picked-up image or the adherent cell in the unknown attribute picked-up image is classified by the cell classifying step. A candidate classification that is a candidate for the classification model using a morphological feature amount of the characteristic object and a plurality of attributes of adhesion-related cells recognized as the characteristic object. The characteristic object extracted from the model A classification model generation step of determining the classification model from among the plurality of candidate classification models generated according to the classification model, wherein the classification model formation step includes a morphological feature amount of the characteristic object As a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, and when assuming that the adherent cells are pseudo linear A plurality of attributes including a name, a culture condition, and an activity are used as the plurality of attributes of the adherent cell recognized as the characteristic object using at least one morphological feature amount of the value indicating the width. And forming the candidate classification model.

  According to the present invention, the type, state, etc. of a cell can be confirmed without destroying the cell and inexpensively and easily.

It is an example of the functional block diagram of the classification model production | generation apparatus 1 by the 1st Embodiment of this invention. 6 is an explanatory diagram for explaining an operation of a noise estimation unit 24. FIG. It is a figure which shows the outline | summary of each feature value. 4 is an example of information stored in a cell object storage unit 72. 4 is an example of information stored in an extraction condition storage unit 74. It is an example of the information memorize | stored in the candidate classification | category model memory | storage part. 4 is an example of information stored in a classification model storage unit 80. It is a block diagram which shows the outline | summary of the incubator 311 connected with the classification model production | generation apparatus 1. FIG. It is a front view of the incubator 311. It is a top view of the incubator 311. 3 is a flowchart showing an example of the operation of the classification model generation device 1. 5 is a flowchart illustrating an example of the operation of a noise estimation unit 24. It is an example of the functional block diagram of the classification model production | generation apparatus 2 by the 2nd Embodiment of this invention. It is an example of the functional block diagram of the cell classification device 3 by the 3rd Embodiment of this invention. 4 is an example of information stored in a search object temporary storage unit 170. It is an example of the information memorize | stored in the cell catalog memory | storage part 190. FIG. 5 is a flowchart showing an example of the operation of the cell classification device 3. It is an example of the functional block diagram of the cell classification device 4. 7 is an example of information stored in a search object temporary storage unit 270. 5 is a flowchart showing an example of the operation of the cell classification device 4. It is a flowchart which shows an example of operation | movement of the incubator 311 which has a function of the cell classification apparatus 4. FIG.

(First embodiment)
Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is an example of a functional block diagram of a classification model generation device 1 according to the first embodiment of the present invention. FIG. 2 is an explanatory diagram for explaining the operation of the noise estimation unit 24. FIG. 3 is a diagram showing an outline of each feature value. FIG. 4 is an example of information stored in the cell object storage unit 72. FIG. 5 is an example of information stored in the extraction condition storage unit 74. FIG. 6 is an example of information stored in the candidate classification model storage unit 76. FIG. 7 is an example of information stored in the classification model storage unit 80. The classification model generation device 1 converts an unknown attribute captured image, which is a captured image obtained by capturing an unknown cell (hereinafter referred to as “unknown attribute captured image”), or a cell in the unknown attribute captured image into a morphological form of the cell. A classification model is generated for classification for each attribute based on the feature.

  As shown in FIG. 1, the classification model generation device 1 includes an input / output unit 10, a cell object recognition unit 20, a classification model generation unit 30, a cell object storage unit 72, an extraction condition storage unit 74, a candidate classification model storage unit 76, and A classification model storage unit 80 is provided. The cell object recognition unit 20 includes a binarization processing unit 22, a noise estimation unit 24, and a cell object feature amount calculation unit 26. The classification model generation unit 30 includes a characteristic object extraction unit 32, a candidate classification model formation unit 34, and a classification model determination unit 36.

  The input / output unit 10 obtains, from the outside, a captured image obtained by capturing a plurality of cells having a known identical attribute (hereinafter referred to as “known identical-attribute captured image”) together with attribute information related to the cell, and a cell object recognizing unit 20 to the binarization processing unit 22. The attribute information is information including the name of the cell, culture conditions, and the like. Accordingly, the cells imaged in the known identical attribute captured image are all cells having the same name, culture conditions, and the like. The outside from which the known identical attribute captured image is acquired is, for example, an incubator 311. The incubator 311 will be described later.

  The input / output unit 10 may acquire a known identical attribute captured image from the outside together with the attribute information and the imaging condition information, and output the acquired image to the binarization processing unit 22. The imaging condition information is information including the observation magnification, the aberration of the optical system of the microscope, the observation point in the container, and the like.

  The cell object recognizing unit 20 recognizes an imaging region of each cell in the known identical attribute captured image as a cell object. That is, the binarization processing unit 22 of the cell object recognition unit 20 binarizes the known identical attribute captured image, and the noise estimation unit 24 of the cell object recognition unit 20 performs data after binarization processing (hereinafter referred to as “binary processing”). An object related to noise (hereinafter referred to as a “noise object”) is estimated from all objects obtained from “binary data”), and the cell object feature amount calculation unit 26 determines an object other than the noise object (ie, an object other than the noise object). Cell object). Hereinafter, the binarization processing unit 22, the noise estimation unit 24, and the cell object feature amount calculation unit 26 will be described in order.

  The binarization processing unit 22 binarizes the known identical attribute captured image acquired from the input / output unit 10 with a predetermined threshold. The predetermined threshold may be in accordance with the type of cell. For example, the binarization processing unit 22 captures an image of an adhesive cell that is a cell that adheres to a culture container in a known identical-attribute captured image in which a cell region is captured in black compared to other regions. A binarization process is performed on a known identical attribute captured image using two threshold values. More specifically, the binarization processing unit 22 uses the pixel value 84 as one threshold value, a region less than the pixel value 84 is a cell region (for example, inverted white), and a region greater than the pixel value 84 is a cell. Regions other than (for example, black by inversion). Further, for example, the binarization processing unit 22 is a cell that floats in the culture solution without being fixed to the culture vessel in a known identical-attribute captured image in which the cell region is imaged blacker than other regions. When a floating cell is imaged, a binarized process is performed on a known identical attribute captured image using two threshold values. More specifically, the binarization processing unit 22 uses the pixel values 80 and 149 as the two threshold values, converts the region of the pixel value 80 or more and less than 149 to the cell region (for example, inverted white), and the pixel value of less than 80. Alternatively, the region of 149 or more is set as a region other than the cell (for example, inverted to black). Note that the binarization processing unit 22 uses the input / output unit 10 to send information indicating whether the cells imaged in the known identical attribute captured image are adhesive cells or floating cells to the incubator 311. And the threshold value may be determined.

  The binarization processing unit 22 that binarized the known identical attribute captured image outputs the binarized data to the noise estimation unit 24 together with the attribute information acquired from the input / output unit 10. The binarization processing unit 22 outputs the binarized data to the noise estimation unit 24 together with the attribute information and the imaging condition information when acquiring the imaging condition information from the input / output unit 10.

  The noise estimation unit 24 estimates a noise object from all objects obtained from the binarized data acquired from the binarization processing unit 22. Hereinafter, a noise object estimation method by the noise estimation unit 24 will be described. The noise estimation unit 24 first measures the object size of each object obtained from the binarized data acquired from the binarization processing unit 22.

  The noise estimation unit 24 that measures the object size of each object calculates a cumulative degree that is the frequency of the cumulative value of the object size up to one object size with respect to the total object size of each object. For example, the noise estimation unit 24 generates a cumulative distribution as shown in FIG. In the cumulative degree distribution shown in FIG. 2A, the X-axis direction is the object size in units of pixels, and the Y-axis direction is the cumulative degree in units.

  The noise estimation unit 24 that has calculated the cumulative degree estimates a noise object based on the calculated differential value of the cumulative degree. For example, as shown in FIG. 2B, the noise estimation unit 24 calculates a differential value (slope) of the cumulative distribution shown in FIG. 2A, and estimates a noise object based on the differential value. . Specifically, the noise estimation unit 24 searches for the minimum value of the differential value, and determines the minimum value or the vicinity of the minimum value as a threshold value. For example, the noise estimation unit 24 may determine the minimum value (value A in FIG. 2B) or a value near the minimum value and smaller than the minimum value (value (A−α in FIG. 2B)). )) In the vicinity of the minimum value and larger than the minimum value (value (A + α) in FIG. 2B) is determined as the threshold value. From experience, it is preferable that the threshold value is a value near the minimum value and smaller than the minimum value.

  The noise estimation unit 24 estimates an object whose object size is less than the threshold as a noise object. Alternatively, the noise estimation unit 24 assigns a label indicating that the object size is less than the threshold value to the object that is a noise object, and assigns a label indicating that the object size is a cell object to an object that the object size is equal to or greater than the threshold value.

  Note that the words “noise” and “cell” at the top of FIGS. 2A and 2B are more likely to be noise objects when the object size is smaller than the value (A−α). The object size equal to or larger than the value (A + α) indicates that the larger the object size, the higher the possibility of being a cell object.

  The noise estimation unit 24 that estimated the noise object from all the objects obtained from the binarized data includes the binarized data and information indicating the estimation result by the noise estimation unit 24 (hereinafter referred to as “estimation result”) along with the attribute information. Information ”) is output to the cell object feature amount calculation unit 26. An example of the estimation result information is information indicating the area of the noise object, or information indicating the area of each object to which a label indicating that the object is a noise object or a cell object is given. Note that when the imaging condition information is acquired from the binarization processing unit 22, the noise estimation unit 24 sends the binarized data and the estimation result information to the cell object feature amount calculation unit 26 together with the attribute information and the imaging condition information. Output.

  The noise estimation unit 24 may search for the minimum value of the differential value using an extreme value search method such as the steepest descent method, but searches for the minimum value of the differential value in a range where the object size is equal to or larger than a predetermined value. May be. Specifically, it is preferable that the range of a predetermined value or more is 100 pixels or more based on the result of comparison and verification of several types of images.

  Moreover, the noise estimation part 24 may calculate a differential value using two points | pieces of a predetermined space | interval among each point of the object size and accumulation value which comprise accumulation degree. For example, the noise estimator 24 may set the differentiation of the cumulative degree distribution as points, that is, the point at which the slope is calculated as 10-point intervals.

  Based on the binarized data and the estimation result information acquired from the noise estimating unit 24, the cell object feature amount calculating unit 26 removes the noise object from all the objects obtained from the binarized data. In other words, the cell object feature amount calculation unit 26 recognizes individual cell objects other than the noise object from the binarized data acquired from the noise estimation unit 24.

  Subsequently, the cell object feature amount calculation unit 26 calculates the morphological feature amount of each recognized cell object. As an example, the cell object feature amount calculation unit 26 calculates the following 15 types of morphological feature amounts for each cell object.

・ Total area (see (a) of FIG. 3)
“Total area” is a value indicating the area of the cell of interest. For example, the cell object feature amount calculation unit 26 can calculate the value of “Total area” based on the number of pixels in the cell area of interest.

・ Hole area (see (b) of FIG. 3)
“Hole area” is a value indicating the area of Hole in the cell of interest. Here, Hole refers to a portion where the brightness of the image in the cell is equal to or greater than a threshold value due to contrast (a portion that is close to white in phase difference observation). For example, lysosomes of intracellular organelles are detected as Hole. Further, depending on the image, a cell nucleus and other organelles can be detected as Hole. Note that the cell object feature amount calculation unit 26 may detect a group of pixels in which the luminance value in the cell is equal to or greater than the threshold as Hole, and calculate the value of “Hole area” based on the number of pixels of the Hole.

・ Relative hole area (see (c) of FIG. 3)
“Relative hole area” is a value obtained by dividing the value of “Hole area” by the value of “Total area” (relative hole area = Hole area / Total area). The “relative hole area” is a parameter indicating the ratio of the organelle in the cell size, and the value varies depending on, for example, enlargement of the organelle or deterioration of the shape of the nucleus.

・ Perimeter (see Fig. 3 (d))
“Perimeter” is a value indicating the length of the outer periphery of the cell of interest. For example, the cell object feature amount calculation unit 26 can acquire the value of “Perimeter” by the contour tracking process when extracting cells.

Width (see (e) of FIG. 3)
“Width” is a value indicating the length of the cell of interest in the horizontal direction (X direction) of the image.

・ Height (see (f) of FIG. 3)
“Height” is a value indicating the length of the cell of interest in the image vertical direction (Y direction).

-Length (see (g) of FIG. 3)
“Length” is a value indicating the maximum value (the total length of the cell) of the lines crossing the cell of interest.

・ Breadth (see (h) of FIG. 3)
“Breadth” is a value indicating the maximum value (lateral width of the cell) of lines orthogonal to “Length”.

・ Fiber Length (see (i) of FIG. 3)
“Fiber Length” is a value indicating the length when the target cell is assumed to be pseudo linear. The cell object feature amount calculation unit 26 calculates the value of “Fiber Length” by the following equation (1).

  However, in the formulas in this specification, “P” indicates the value of Perimeter. Similarly, “A” indicates the value of Total Area.

・ Fiber Breath (see (j) of FIG. 3)
“Fiber Breath” is a value indicating a width (a length in a direction orthogonal to Fiber Length) when the target cell is assumed to be pseudo linear. The cell object feature amount calculation unit 26 calculates the value of “Fiber Breath” by the following equation (2).

Shape Factor (see (k) in FIG. 3)
“Shape Factor” is a value indicating the circularity of the cell of interest (cell roundness). The cell object feature amount calculation unit 26 calculates the value of “Shape Factor” by the following equation (3).

Elliptical form factor (see (l) in FIG. 3)
“Elliptical form Factor” is a value obtained by dividing the value of “Length” by the value of “Breadth” (Elliptical form Factor = Length / Breadth), which indicates the degree of slenderness (ellipticity) of the cell of interest. It becomes a parameter.

・ Inner radius (see (m) of FIG. 3)
“Inner radius” is a value indicating the radius of the inscribed circle of the cell of interest.

・ Outer radius (see (n) of FIG. 3)
“Outer radius” is a value indicating the radius of the circumscribed circle of the cell of interest.

・ Mean radius (see (o) of FIG. 3)
“Mean radius” is a value indicating the average distance between all the points constituting the outline of the cell of interest and its center of gravity.

Equivalent radius (see (p) of FIG. 3)
“Equivalent radius” is a value indicating the radius of a circle having the same area as the cell of interest. The parameter “Equivalent radius” indicates the size when the cell of interest is virtually approximated to a circle.

  Here, the cell object feature value calculation unit 26 may calculate each of the morphological feature values by adding an error to the number of pixels corresponding to the cell. At this time, the cell object feature amount calculation unit 26 may calculate the morphological feature amount in consideration of imaging conditions of the microscope image (observation magnification, aberration of the optical system of the microscope, and the like). When calculating “Inner radius”, “Outer radius”, “Mean radius”, and “Equivalent radius”, the cell object feature amount calculation unit 26 calculates the center of gravity of each cell based on a known center of gravity calculation method. What is necessary is just to obtain | require and calculate each parameter on the basis of this barycentric point.

  The cell object feature amount calculation unit 26 that has calculated the morphological feature amount of each cell object stores the morphological feature amount of each cell object in the cell object storage unit 72 in association with the attribute information. When the cell object feature value calculation unit 26 acquires the imaging condition information from the noise estimation unit 24, the cell object feature value calculation unit 26 associates the morphological feature value of each cell object with the cell object storage in association with the attribute information and the imaging condition information. Store in the unit 72.

The cell object storage unit 72 stores information related to cell objects recognized from a plurality of known identical attribute captured images obtained by imaging cells having various attributes. Specifically, as shown in FIG. 4, the cell object storage unit 72 associates attribute information, imaging condition information, and morphological characteristics of each cell object with an image ID that identifies known identical attribute imaging. Remember the amount. For example, in the example illustrated in FIG. 4, the cell object storage unit 72 associates the image ID “P001” with the attribute information including the name “A”, the culture condition “OO”, and the activity “XX”, and the observation magnification. The imaging condition information including “X times” and the morphological feature amount of N ₁ cell objects are stored.

  The extraction condition storage unit 74 includes a characteristic object (hereinafter referred to as “candidate classification model”) that is used by the characteristic object extraction unit 32 to generate a model that is a classification model candidate (hereinafter referred to as “candidate classification model”) from the cell object storage unit 72. The extraction condition when extracting “characteristic object”) is stored. For example, as illustrated in FIG. 5, the extraction condition storage unit 74 stores extraction condition information related to a plurality of morphological feature amounts of individual cell objects. In the example shown in FIG. 5, the extraction condition storage unit 74 has the extraction condition 1 “extract the upper 25% from the average value”, the extraction condition 2 “extract the lower 25% from the average value”,. As extraction condition 5, “extract 25% higher than average value” is stored, and as extraction condition 6, “extract 25% lower than average value” is stored.

  In the example shown in FIG. 5, the extraction condition storage unit 74 stores extraction conditions relating to three types of morphological feature amounts of “area”, “length”, and “width”, but the cell object storage unit It is preferable to store extraction conditions relating to all the morphological feature amounts corresponding to the morphological feature amounts stored in 72.

  Further, the extraction condition shown in FIG. 5 uses the average value as a reference value, but may use a value other than the average value such as a maximum value or a median value as a reference. Further, in the extraction condition shown in FIG. 5, the value defining the predetermined range is 25%, but may be a value other than 25%. Further, for example, as shown in the extraction condition 1 and the extraction condition 2, for example, the extraction condition shown in FIG. 5 has the same value for defining the predetermined range for the same morphological feature amount. The same morphological feature amount within a predetermined range, such as “extract the upper 20% of the area from the average value” as the extraction condition 1 and “extract the lower 30% of the area from the average value” as the extraction condition 2 Different values may be specified. Further, for example, in the extraction condition shown in FIG. 5, for example, the value that defines the predetermined range is the same value for any morphological feature amount. For example, as the extraction condition 1, “the area is higher than the average value” For example, “extracting 20%” and “extracting 30% higher than the average value” as the extraction condition 5 may be different values for different morphological feature values that define the predetermined range.

  The classification model generation unit 30 generates a classification model based on the morphological feature amount of the cell object and the attribute of the cell recognized as the cell object. That is, the characteristic object extraction unit 32 of the classification model generation unit 30 extracts a cell object having a predetermined morphological feature amount from the cell objects stored in the cell object storage unit 72 as a characteristic object, and classifies it. The candidate classification model formation unit 34 of the model generation unit 30 forms a plurality of candidate classification models from the morphological feature amounts of the characteristic objects and the attributes of the cells recognized as the characteristic objects, and the classification model determination unit 36 includes the plurality of classification model determination units 36. A classification model is determined from the candidate classification models. Hereinafter, the characteristic object extraction unit 32, the candidate classification model formation unit 34, and the classification model determination unit 36 will be described in order.

  The characteristic object extraction unit 32 extracts a cell object having a morphological characteristic value within a predetermined range from the reference value as a characteristic object. Specifically, the characteristic object extraction unit 32 extracts a characteristic object from a plurality of cell objects stored in the cell object storage unit 72 according to the extraction conditions stored in the extraction condition storage unit 74. To do. More specifically, the characteristic object extraction unit 32 sequentially extracts the extraction conditions stored in the extraction condition storage unit 74 and extracts from the plurality of cell objects stored in the cell object storage unit 72. A characteristic object is extracted for each condition.

There are various modes in which the characteristic object extracting unit 32 extracts the characteristic objects. For example, only N ₁ cell objects, N ₂ cell objects, and N ₃ cell objects shown in FIG. 4 are stored in the cell object storage unit 72, and the extraction condition information shown in FIG. Several extraction modes will be described by taking as an example the case where is stored.

(First extraction mode)
The characteristic object extraction unit 32 first calculates an average area value, an average length value, and an average width value of (N ₁ + N ₂ + N ₃ ) cell objects. Next, the characteristic object extraction unit 32 satisfies either one of the extraction conditions 1 and 2 which are extraction conditions related to the area among (N ₁ + N ₂ + N ₃ ) cell objects, and is long. A cell object that satisfies any one of the extraction conditions 3 and 4 that are extraction conditions related to the depth and that satisfies the extraction condition 5 or 6 that is the extraction conditions related to the width is defined as a characteristic object. Extract. For example, the characteristic object extraction unit 32 has an area within a range of an average value to a value 25% larger than the average value (extraction condition 1) among (N ₁ + N ₂ + N ₃ ) cell objects, In addition, the length is within the range of the average value to 25% smaller than the average value (extraction condition 4), and the width is within the range of the average value to 25% larger than the average value (extraction condition 5). A certain cell object is extracted as a characteristic object. The AND condition (that is, “and”) may be OR (that is, “or”). In the first extraction mode, the number of patterns for extracting a characteristic object from a cell object is a {2 morphological feature quantity} power pattern. For example, in the case of the above example, since the morphological feature amount is 3, there are 8 patterns.

(Second extraction mode)
The characteristic object extraction unit 32 calculates an average value of the area of (N ₁ + N ₂ + N ₃ ) cell objects according to the extraction condition 1, and from among the (N ₁ + N ₂ + N ₃ ) cell objects, A cell object whose area is in the range of an average value to a value 25% larger than the average value is extracted as a characteristic object. The same applies to extraction conditions 2 to 6. In the second extraction mode, the number of patterns for extracting characteristic objects from cell objects is 2 × {number of morphological feature values} patterns. For example, in the case of the above example, since the morphological feature amount is 3, there are 6 patterns.

(Third extraction mode)
The characteristic object extraction unit 32 calculates an average value of the areas of the N ₁ , N ₂ , and N ₃ cell objects according to the extraction condition 1 set in advance among the extraction conditions 1 to 6, and N ₁ Among the cell objects, a cell object whose area is in the range of an average value to a value 25% larger than the average value is extracted as a characteristic object, and the area is an average value to an average value among the N ₂ cell objects. A cell object within a range of 25% larger value is extracted as a characteristic object, and a cell object whose area is within a range of an average value to a value 25% larger than the average value is selected from N ₃ cell objects. You may extract as a characteristic object. The same applies when the extraction conditions 2 to 6 are used. In the third extraction mode, the number of patterns for extracting characteristic objects from cell objects is a 2 × {number of images} power pattern. For example, in the case of the above example, since the number of images is 3, there are 8 patterns.

In the description of the extraction modes 1, 2, and 3, the characteristic object extraction unit 32 uses, as the characteristic object, a cell object whose area is in the range of the average value to a value 25% larger than the average value according to the extraction condition 1. Although extracted, the characteristic object extraction unit 32 calculates an average value of the areas of (N ₁ + N ₂ + N ₃ ) cell objects in accordance with the extraction condition 1 and calculates (N ₁ + N ₂ + N ₃ ) Cell objects having an area of the average value or more of the cell objects may be set as a population, and cell objects within 25% from the smaller area of the population may be extracted as characteristic objects. In addition, the characteristic object extraction unit 32 may extract cell objects included in the population from the smallest area ((N ₁ + N ₂ + N ₃ ) / 4) as characteristic objects. The same applies to the extraction conditions 2 to 6.

  When the characteristic object extraction unit 32 extracts the characteristic object from the cell objects stored in the cell object storage unit 72, the characteristic object extraction unit 32 uses the attribute information and the morphological characteristic amount of each characteristic object as a candidate classification model formation unit. 34. For example, each time the characteristic object is extracted by changing the extraction condition, the characteristic object extraction unit 32 extracts the characteristic object attribute information and the morphological characteristic amount extracted according to each extraction condition as the candidate classification model formation unit 34. Output to.

  The candidate classification model forming unit 34 includes the morphological feature amount of the characteristic object and the attribute of the cell recognized as the characteristic object, that is, the attribute information and the morphological feature of the characteristic object acquired from the characteristic object extraction unit 32. A candidate classification model is formed (generated) using the quantity.

  Hereinafter, a method for forming a candidate classification model by the candidate classification model forming unit 34 will be described. The candidate classification model forming unit 34 sets classification criteria (the type and threshold value of the morphological feature amount) related to the morphological feature amount obtained from the characteristic object acquired from the characteristic object extraction unit 32, and forms a candidate classification model. To do. In other words, the candidate classification model forming unit 34 sets (selects) the classification standard (the type and the threshold value of the morphological feature value) constituting the candidate classification model from the morphological feature values obtained from the characteristic object. Thus, a candidate classification model is formed.

  The first and Mth candidate classification models shown in FIG. 6 are examples of candidate classification models formed by the candidate classification model forming unit 34 as described above. The first candidate classification model is classified by the candidate classification model forming unit 34 into the first stage classification standard (morphological feature amount “length” and threshold “12”), and the second stage classification standard (morphological feature). The candidate classification model and the Mth candidate classification model in which the amount “roundness” and the threshold value “0.7” are set are classified by the candidate classification model forming unit 34 into the first-stage classification criterion (morphological feature quantity “ Roundness ”and threshold value“ 12 ”), and the second-stage classification criteria (morphological feature amounts“ width ”and“ 1.2 ”) are set. The candidate classification model forming unit 34 stores each formed candidate classification model in the candidate classification model storage unit 76. Note that the candidate classification model in which the classification criteria are set in multiple stages as shown in FIG. 6 classifies the unknown attribute captured image or the cells in the unknown attribute captured image in stages. A method for forming the classification model will be described later.

  The candidate classification model forming unit 34 sets a classification destination ID (also referred to as a classification index) indicating a cell classification destination in each branch of the candidate classification model. Each classification destination ID corresponds to cell attribute information. Therefore, the attribute information of the cell in the unknown attribute captured image or the unknown attribute captured image can be obtained from the unknown attribute captured image or the cell classification destination ID in the unknown attribute captured image. In FIG. 6, in the first candidate classification model and the Mth candidate classification model, the classification destination ID “N0001” has the name “A”, the culture condition “OO”, and the activity “XX” in FIG. The classification destination ID “N0002” corresponds to the attribute information including the name “B”, the culture condition “ΔΔ”, and the activity “□□”, and the classification destination ID “N0003” has the name “B”. Corresponds to attribute information including “C”, culture condition “「 ”, and activity“ □□ ”. In order to associate the classification destination ID with the attribute, the cell object feature amount calculation unit 26 associates the morphological feature amount of each cell object with the cell object in association with new attribute information (attribute information and imaging condition information). When storing in the storage unit 72, the classification destination ID may be numbered, and the classification destination ID numbered in association with the attribute information (attribute information and imaging condition information) may be stored.

  Hereinafter, a method of forming a candidate classification model that classifies the attributes of cell objects in stages will be described. The candidate classification model forming unit 34 forms a candidate classification model for variable selection based on the decision tree method. For example, ten characteristic objects having an attribute A, ten characteristic objects having an attribute B, and ten characteristic objects having an attribute C are given, and the morphological feature amount (dimension) of each characteristic object is given. Are 15 types (feature amount 1, feature amount 2,..., Feature amount 15), the candidate classification model forming unit 34 first increases the value of feature amount 1 from the minimum to the maximum, The value of the feature amount 1 when the three types of characteristic objects A, B, and C are best classified is specified.

  For example, the candidate classification model forming unit 34 has, for a certain value of the feature amount 1, a certain object (branch destination) has 9 of 10 characteristic objects A and 1 of 10 characteristic objects B. When there is one object C out of ten, the classification accuracy of the node is calculated as 90%. Similarly, the candidate classification model forming unit 34 calculates the classification accuracy of all nodes for the value of the feature quantity 1. Since the classification accuracy of each node becomes higher when each characteristic object is assigned to each node, the candidate classification model forming unit 34 calculates the average classification accuracy of all nodes in the value of the feature amount 1. . Similarly, the candidate classification model forming unit 34 increases the value of the feature quantity 1 and calculates the average classification accuracy of all nodes at each feature quantity 1 value. Then, the candidate classification model forming unit 34 uses the feature quantity 1 having the highest average classification accuracy of all nodes as the value of the feature quantity 1 when the three types of characteristic objects A, B, and C are best classified. Identify the value.

  Similarly, the candidate classification model formation unit 34 specifies the value of feature quantity 2, the value of feature quantity 3,..., The value of feature quantity 15 having the highest average classification accuracy of all nodes. Then, the candidate classification model forming unit 34 calculates the average of all nodes as a first-stage rule in the candidate classification model from among the identified feature value 1, feature value 2 value,..., Feature value 15 value. Select the one with the highest classification accuracy. Similarly, the candidate classification model forming unit 34 selects a rule after the second stage.

  The classification model determination unit 36 inputs the cell object stored in the cell object storage unit 72 into each candidate classification model, and evaluates the classification accuracy of each candidate classification model. The classification model determination unit 36 may input a known identical attribute captured image into each candidate classification model and evaluate the classification accuracy of each candidate classification model. Further, the classification model determination unit 36 determines the candidate classification model with the highest accuracy as a classification model to be used for the subsequent classification, and stores it in the classification model storage unit 80. For example, as shown in FIG. 7, the classification model determination unit 36 may store the calculated classification accuracy evaluation value in the classification model storage unit 80 together with the determined classification model.

  For example, the arrows in FIG. 1 indicate the relationship between the characteristic object extraction by the characteristic object extraction unit 32, the formation of the candidate classification model by the candidate classification model formation unit 34, and the determination of the classification model by the classification model determination unit 36. Although omitted, it may be realized as follows.

  After storing the morphological feature amount in the cell object storage unit 72, the cell object feature amount calculation unit 26 notifies the candidate classification model forming unit 34 that the morphological feature amount has been stored. When the candidate classification model forming unit 34 obtains a notification that the morphological feature value is stored in the cell object storage unit 72 from the cell object feature value calculation unit 26, the candidate classification model formation unit 34 requests the characteristic object extraction unit 32 to perform extraction.

  When the candidate classification model forming unit 34 requests extraction, the characteristic object extracting unit 32 extracts the characteristic object according to the unextracted pattern when the characteristic object has not yet been extracted according to the entire extracted pattern. The result is output to the candidate classification model forming unit 34. On the other hand, if the characteristic object extraction unit 32 has already extracted characteristic objects according to the entire extraction pattern, the characteristic object extraction unit 32 notifies the candidate classification model formation unit 34 that extraction has been completed.

  When the candidate classification model formation unit 34 acquires a characteristic object from the characteristic object extraction unit 32, the candidate classification model formation unit 34 forms a candidate classification model, stores the candidate classification model in the candidate classification model storage unit 76, and again stores the characteristic object extraction unit 32 in the characteristic object extraction unit 32. Request extraction. On the other hand, when the candidate classification model forming unit 34 obtains notification from the characteristic object extraction unit 32 that extraction has been completed, the candidate classification model formation unit 34 requests the classification model determination unit 36 to determine a classification model. When the classification model determination unit 36 is requested by the candidate classification model formation unit 34 to determine a classification model, the classification model determination unit 36 evaluates the candidate classification model and determines a classification model.

  Hereinafter, the incubator 311 will be described. FIG. 8 shows an example of an incubator 311 connected to the classification model generation device 1. 9 and 10 are front views of the incubator 311. FIG.

  The incubator 311 has an upper casing 312 and a lower casing 313. In the assembled state of the incubator 311, the upper casing 312 is placed on the lower casing 313. Note that the internal space between the upper casing 312 and the lower casing 313 is vertically divided by a base plate 314.

  First, the outline of the configuration of the upper casing 312 will be described. A constant temperature room 315 for culturing cells is formed inside the upper casing 312. The temperature-controlled room 315 includes a temperature adjusting device 315a and a humidity adjusting device 315b, and the temperature-controlled room 315 is maintained in an environment suitable for cell culture (for example, an atmosphere having a temperature of 37 ° C. and a humidity of 90%) ( Note that illustration of the temperature adjusting device 315a and the humidity adjusting device 315b in FIGS. 9 and 10 is omitted).

  A large door 316, a middle door 317, and a small door 318 are arranged on the front surface of the temperature-controlled room 315. The large door 316 covers the front surfaces of the upper casing 312 and the lower casing 313. The middle door 317 covers the front surface of the upper casing 312 and isolates the environment between the temperature-controlled room 315 and the outside when the large door 316 is opened. The small door 318 is a door for carrying in / out a culture vessel 319 for culturing cells, and is attached to the middle door 317. By carrying in / out the culture vessel 319 from this small door 318, it becomes possible to suppress the environmental change of the temperature-controlled room 315. The large door 316, the middle door 317, and the small door 318 are kept airtight by the packings P1, P2, and P3, respectively.

  In the temperature-controlled room 315, a stocker 321, an observation unit 322, a container transport device 323, and a transport base 324 are arranged. Here, the transfer table 324 is disposed in front of the small door 318, and carries the culture container 319 out of the small door 318.

  The stocker 321 is disposed on the left side of the temperature-controlled room 315 when viewed from the front surface of the upper casing 312 (the lower side in FIG. 10). The stocker 321 has a plurality of shelves, and each shelf of the stocker 321 can store a plurality of culture vessels 319. Each culture container 319 contains cells to be cultured together with a medium.

  The observation unit 322 is disposed on the right side of the temperature-controlled room 315 when viewed from the front surface of the upper casing 312. This observation unit 322 can execute time-lapse observation of cells in the culture vessel 319.

  Here, the observation unit 322 is disposed by being fitted into the opening of the base plate 314 of the upper casing 312. The observation unit 322 includes a sample stage 331, a stand arm 332 projecting above the sample stage 331, and a main body portion 333 incorporating a microscopic optical system for phase difference observation and an imaging device 334. The sample stage 331 and the stand arm 332 are disposed in the temperature-controlled room 315, while the main body portion 333 is accommodated in the lower casing 313.

  The sample stage 331 is made of a translucent material, and a culture vessel 319 can be placed thereon. The sample stage 331 is configured to be movable in the horizontal direction, and the position of the culture vessel 319 placed on the upper surface can be adjusted. The stand arm 332 has a built-in LED light source 335. The imaging device 334 can acquire a microscopic image of the cells by imaging the cells in the culture vessel 319 that are transmitted and illuminated from above the sample stage 331 by the stand arm 332 via the microscopic optical system.

  The container transport device 323 is disposed at the center of the temperature-controlled room 315 when viewed from the front surface of the upper casing 312. The container transport device 323 delivers the culture container 319 between the stocker 321, the sample table 331 of the observation unit 322, and the transport table 324.

  As shown in FIG. 10, the container transport device 323 includes a vertical robot 334 having an articulated arm, a rotary stage 335, a ministage 336, and an arm portion 337. The rotary stage 335 is attached to the tip of the vertical robot 334 via a rotary shaft 335a so that it can rotate 180 ° in the horizontal direction. Therefore, the rotation stage 335 can make the arm unit 337 face the stocker 321, the sample table 331, and the transport table 324, respectively.

  The mini stage 336 is attached to the rotary stage 335 so as to be slidable in the horizontal direction. An arm portion 337 for holding the culture vessel 319 is attached to the mini stage 336.

  Next, an outline of the configuration of the lower casing 313 will be described. In the lower casing 313, the main body portion 333 of the observation unit 322 and the control device 341 of the incubator 311 are housed.

  The control device 341 is connected to the temperature adjustment device 315a, the humidity adjustment device 315b, the observation unit 322, and the container transport device 323, respectively. The control device 341 comprehensively controls each part of the incubator 311 according to a predetermined program.

  As an example, the control device 341 includes a CPU 342 and a storage unit 343, and controls the temperature adjustment device 315a and the humidity adjustment device 315b, respectively, to maintain the inside of the temperature-controlled room 315 at predetermined environmental conditions. The control device 341 controls the observation unit 322 and the container transport device 323 based on a predetermined observation schedule, and automatically executes the observation sequence of the culture vessel 319.

  The storage unit 343 is configured with a non-volatile storage medium such as a hard disk or a flash memory. The storage unit 343 stores management data regarding each culture vessel 319 stored in the stocker 321 and data of a microscope image captured by the imaging device. Further, the storage unit 343 stores a program executed by the CPU 342.

  The management data includes (a) index data indicating individual culture containers 319, (b) the storage position of the culture container 319 in the stocker 321 and (c) the type and shape of the culture container 319 (well plate, (Dish, flask, etc.), (d) type of cells cultured in culture vessel 319, (e) observation schedule of culture vessel 319, (f) imaging conditions during time-lapse observation (magnification of objective lens, observation in vessel) Point, etc.). In addition, regarding the culture container 319 that can simultaneously culture cells in a plurality of small containers such as a well plate, management data is generated for each small container.

  Hereinafter, the operation of the classification model generation device 1 will be described using a flowchart. FIG. 11 is a flowchart illustrating an example of the operation of the classification model generation device 1. FIG. 12 is a flowchart illustrating an example of the operation of the noise estimation unit 24. In FIG. 11, the input / output unit 10 acquires attribute information, imaging condition information, and a known identical attribute captured image from the outside (step S112). The input / output unit 10 outputs the attribute information, the imaging condition information, and the known identical attribute captured image acquired from the outside to the binarization processing unit 22.

  The binarization processing unit 22 binarizes the known identical attribute captured image acquired from the input / output unit 10 with a predetermined threshold (step S114). The binarization processing unit 22 outputs the binarized data to the noise estimation unit 24 together with the attribute information and the imaging condition information.

  The noise estimation unit 24 acquires the binarized data from the binarization processing unit 22, and estimates the noise object from all the objects obtained from the acquired binarized data (step S120). Specifically, the noise estimation unit 24 executes each operation shown in FIG.

  In FIG. 12, the noise estimation unit 24 acquires binarized data from the binarization processing unit 22 (step S121). Next, the noise estimation unit 24 measures the object size of each object obtained from the binarized data (step S122). Next, the noise estimation unit 24 generates a cumulative distribution representing the frequency of the cumulative value of the object size up to one object size with respect to the total object size of each object (step S123).

  Next, the noise estimation unit 24 searches for the minimum value of the slope (differential value) of each two points at predetermined intervals within a range equal to or greater than the predetermined value in the cumulative distribution (step S124). Next, the noise estimating unit 24 determines a value near the minimum value and smaller than the minimum value as a threshold (step S125). Next, the noise estimation unit 24 estimates an object whose object size is less than the threshold as a noise object (step S126). The noise estimation unit 24 outputs the binarized data and the estimation result information to the cell object feature amount calculation unit 26 together with the attribute information and the imaging condition information. Then, the flowchart shown in FIG. 12 ends.

  Returning to FIG. 11, the cell object feature amount calculation unit 26 that has acquired the binarized data and the estimation result information together with the attribute information and the imaging condition information from the noise estimation unit 24, the binarized data and the estimation acquired from the noise estimation unit 24. Based on the result information, the noise object is removed from all the objects obtained from the binarized data (step S130). In other words, the cell object feature quantity calculation unit 26 recognizes individual cell objects other than the noise object from the binarized data acquired from the noise estimation unit 24 (step S130).

  Next, the cell object feature amount calculation unit 26 calculates the morphological feature amount of each recognized cell object (step S132). The cell object feature amount calculation unit 26 that calculated the morphological feature amount of each cell object associates the calculated morphological feature amount of each cell object with the attribute information and the imaging condition information in the cell object storage unit 72. Store (step S134).

  The input / output unit 10 determines whether or not acquisition of a known identical-attribute captured image or the like has been completed (step S136). For example, when the input / output unit 10 obtains a notification indicating that output of a known identical attribute captured image or the like has been completed from the incubator 311 or a notification indicating that a classification model should be generated, the input / output unit 10 knows the known identical attribute captured image. And so on. If the input / output unit 10 determines that acquisition of a known identical-attribute captured image or the like has not been completed (step S136: No), the process returns to step S112.

  On the other hand, when the input / output unit 10 determines that the acquisition of the known identical attribute captured image or the like has been completed (step S136: Yes), the cell object feature amount calculation unit 26 indicates that the morphological feature amount is stored as a candidate classification model. Notify the forming unit 34. The candidate classification model forming unit 34 that has received the notification that the morphological feature quantity has been stored requests the characteristic object extraction unit 32 to perform extraction. The characteristic object extraction unit 32 requested to extract extracts a characteristic object from the cell objects stored in the cell object storage unit 72 according to one extraction pattern (step S142). The characteristic object extraction unit 32 that has extracted the characteristic object outputs the attribute information and the morphological feature amount of each characteristic object to the candidate classification model formation unit 34.

  The candidate classification model forming unit 34 that acquired the attribute information and the morphological feature amount of each characteristic object forms (generates) a candidate classification model based on the attribute information and the morphological feature amount of each characteristic object. The candidate classification model is stored in the candidate classification model storage unit 76 (step S144). Next, the candidate classification model forming unit 34 determines whether all candidate classification models have been formed (step S146). For example, when the candidate classification model forming unit 34 requests extraction from the characteristic object extraction unit 32 and obtains notification from the characteristic object extraction unit 32 that extraction has been completed, all candidate classification models are formed. Judge. If the candidate classification model forming unit 34 determines that not all candidate classification models have been formed (step S146: No), the process returns to step S142. In step S142 to be executed again, the characteristic object extraction unit 32 extracts characteristic objects according to the unextracted pattern.

  If the candidate classification model forming unit 34 determines that all candidate classification models have been formed (step S146: Yes), it requests the classification model determination unit 36 to determine a classification model. The classification model determination unit 36 requested to determine the classification model determines a classification model from among a plurality of candidate classification models, and stores the classification model in the classification model storage unit 80 (step S150). Then, the flowchart shown in FIG. 11 ends.

(Second Embodiment)
The second embodiment of the present invention will be described below with reference to the drawings. FIG. 13 is an example of a functional block diagram of the classification model generation device 2 according to the second embodiment of the present invention. As shown in FIG. 13, the classification model generation device 2 includes an input / output unit 10, a cell object recognition unit 20, a classification model generation unit 40, a cell object storage unit 72, and a classification model storage unit 80. The classification model generation unit 40 includes a representative feature quantity calculation unit 42 and a classification model formation unit 46. The input / output unit 10, the cell object recognition unit 20, the cell object storage unit 72, and the classification model storage unit 80 are the same as the input / output unit 10 and the cell object recognition of the classification model generation device 1 of the first embodiment shown in FIG. The same as the unit 20, the cell object storage unit 72, and the classification model storage unit 80.

  The representative feature amount calculation unit 42 calculates a representative feature amount that represents the morphological feature amounts of the plurality of cell objects recognized from the known identical attribute captured images. For example, the representative feature amount calculation unit 42 calculates the average value of each morphological feature amount of a plurality of cell objects extracted from the known identical attribute captured image, and calculates the average value of each morphological feature amount as the representative feature amount. And When the representative feature amount calculating unit 42 calculates the representative feature amount from each known identical attribute captured image, the representative feature amount calculated from each known identical attribute captured image, and the attribute information of each known identical attribute captured image Is output to the classification model forming unit 46. In other words, the representative feature quantity calculation unit 42 generates a virtual cell object (hereinafter referred to as “virtual object”) having the representative feature quantity as its morphological feature quantity for each known identical attribute captured image. Then, the attribute information and the morphological feature amount (that is, the representative feature amount) of each virtual object are output to the classification model forming unit 46.

  The classification model formation unit 46 generates a classification model using the attribute information and morphological feature amounts of a plurality of virtual objects for each known identical attribute captured image acquired from the representative feature amount calculation unit 42. The method for generating the classification model by the classification model forming unit 46 is the same as the method for generating the candidate classification model by the candidate classification model forming unit 34 of the classification model generating apparatus 1 according to the first embodiment. The classification model forming unit 46 stores the generated classification model in the classification model storage unit 80.

  Note that the representative feature quantity calculation unit 42 uses the average value of each morphological feature quantity as a representative feature quantity, but other than the average value may be used as the representative feature quantity. For example, the representative feature quantity calculation unit 42 may use the median value and standard deviation of each morphological feature quantity as the representative feature quantity.

(Third embodiment)
The third embodiment of the present invention will be described below with reference to the drawings. FIG. 14 is an example of a functional block diagram of the cell classification device 3 according to the third embodiment of the present invention. FIG. 15 is an example of information stored in the search object temporary storage unit 170. FIG. 16 is an example of information stored in the cell catalog storage unit 190.

  As shown in FIG. 14, the cell classification device 3 includes an input / output unit 110, a cell object recognition unit 120, a characteristic object extraction / representative feature amount calculation unit 152, a cell classification unit 160, a search object one-time billion unit 170, a classification A model storage unit 180 and a cell catalog storage unit 190 are provided. The cell object recognition unit 120 includes a binarization processing unit 122, a noise estimation unit 124, and a cell object feature amount calculation unit 126.

  The input / output unit 110 acquires an unknown attribute captured image from the outside. Specifically, an unknown attribute captured image obtained by imaging a plurality of cells with unknown different attributes (hereinafter referred to as “unknown different attribute captured image”), or an unknown attribute obtained by imaging a plurality of unknown identical attributes. A captured image (hereinafter referred to as an “unknown identical attribute captured image”) is acquired from the outside. The input / output unit 110 outputs the unknown attribute captured image acquired from the outside to the binarization processing unit 122 of the cell object recognition unit 120. The outside from which the unknown attribute captured image is acquired is, for example, an external medium (for example, a USB memory) that can be connected to the cell classification device 3, other devices connected via a network such as a LAN, the Internet, Alternatively, the incubator 311 is used.

  Further, the input / output unit 110 may acquire image type information from the outside together with the unknown attribute captured image and output the image type information to the binarization processing unit 122. The image type information is information for identifying whether the unknown attribute captured image is an unknown heterogeneous attribute captured image or an unknown identical attribute captured image.

  The cell object recognition unit 120 recognizes individual cell objects (hereinafter referred to as “search objects”) in the unknown attribute captured image. Specifically, the binarization processing unit 122 of the cell object recognition unit 120 is the same as the binarization processing unit 22 of the classification model generation device 1 according to the first embodiment described above. The attribute captured image is binarized by a predetermined threshold value. The binarization processing unit 122 that has performed binarization processing outputs the binarized data to the noise estimation unit 124. Note that the binarization processing unit 122 outputs the binarized data and the image type information to the noise estimation unit 124 when the image type information is acquired from the input / output unit 110.

  Similar to the noise estimation unit 24 of the classification model generation device 1 according to the first embodiment, the noise estimation unit 124 is a noise object among all objects obtained from the binarized data acquired from the binarization processing unit 122. Is estimated. The noise estimation unit 124 that has estimated the noise object outputs the estimation result information to the cell object feature amount calculation unit 126 together with the binarized data. When the noise estimation unit 124 acquires the image type information from the binarization processing unit 122, the noise estimation unit 124 outputs the estimation result information to the cell object feature amount calculation unit 126 together with the binarized data and the image type information. To do.

  Based on the binarized data and the estimation result information acquired from the noise estimating unit 124, the cell object feature amount calculating unit 126 removes the noise object from all the objects obtained from the binarized data. In other words, the cell object feature amount calculation unit 126 recognizes individual cell objects other than the noise object from the binarized data acquired from the noise estimation unit 124.

  Subsequently, the cell object feature quantity calculation unit 126 calculates the morphological feature quantity of each of the recognized search objects in the same manner as the cell object feature quantity calculation unit 26 of the classification model generation device 1 according to the first embodiment. . The cell object feature amount calculation unit 126 that has calculated the morphological feature amount of the search object stores the morphological feature amount of each search object in the search object temporary storage unit 170. Note that the cell object feature amount calculation unit 126 stores the image type information and the morphological feature amount of each search object in the search object temporary storage unit 170 when the image type information is acquired from the noise estimation unit 124. To do.

The search object temporary storage unit 170 stores information related to the search object. Specifically, as shown in FIGS. 15A and 15B, the search object temporary storage unit 170 stores morphological feature amounts of individual search objects in association with image type information. In the example shown in FIG. 15A, the search object temporary storage unit 170 associates N ₄ with image type information (for example, value 0) indicating that the search object extraction source is an unknown heterogeneous attribute captured image. The morphological feature amount of each search object is stored. In the example shown in FIG. 15B, the search object temporary storage unit 170 is associated with image type information (for example, value 1) indicating that the search object extraction source is an unknown same-attribute captured image. N stores the morphological features of the _five search objects.

  Note that the initial value of the item “image type information” in the search object temporary storage unit 170 is image type information indicating that the extraction source of the search object is an unknown different-attribute captured image. Therefore, when the cell object feature quantity calculation unit 126 does not acquire the image type information from the noise estimation unit 124, the item “image type information” indicates that the search object extraction source is an unknown heterogeneous attribute captured image. Image type information is stored.

  The extraction condition storage unit 174 stores extraction conditions for extracting a search object from among a plurality of search objects stored in the search object temporary storage unit 170. Hereinafter, the search object extracted from the search object temporary storage unit 170 is referred to as a characteristic search object. The extraction condition storage unit 174 stores extraction conditions having the same contents as the contents stored in the extraction condition storage unit 74 of the classification model generation device 1 according to the first embodiment.

  The characteristic object extraction / representative feature quantity calculation unit 152 includes the characteristic object extraction unit 32 of the classification model generation device 1 according to the first embodiment and the classification model generation device 2 according to the second embodiment. The representative feature amount calculation unit 42 is provided. In other words, the characteristic object extraction / representative feature amount calculation unit 152, when an unknown heterogeneous attribute captured image is input to the cell classification device 3, is characterized by the classification model generation device 1 according to the first embodiment. When processing similar to that performed by the object extraction unit 32 and an unknown identical attribute captured image is input to the cell classification device 3, the representative feature amount calculation unit 42 of the classification model generation device 2 according to the second embodiment described above. The same processing is performed.

  Specifically, the characteristic object extraction / representative feature amount calculation unit 152 stores image type information indicating that the search object extraction source is an unknown heterogeneous attribute captured image in the search object temporary storage unit 170. If there is, the characteristic search object is extracted from the search objects stored in the search object temporary storage unit 170 in accordance with the extraction conditions stored in the extraction condition storage unit 174. Note that the feature object extraction / representative feature amount calculation unit 152 may acquire information indicating which feature search object is extracted according to which extraction condition from the outside via the input / output unit 110. When the characteristic object extraction / representative feature quantity calculation unit 152 extracts characteristic search objects from the search objects stored in the search object temporary storage unit 170, the attribute information and morphological features of each characteristic search object are extracted. The amount is output to the cell classification unit 160.

  On the other hand, the characteristic object extraction / representative feature amount calculation unit 152 stores, in the search object temporary storage unit 170, image type information indicating that the search object extraction source is an unknown identical attribute captured image. Calculates a representative feature amount representative of the morphological feature amount of the search object from the search object stored in the search object temporary storage unit 170. When the representative object extraction / representative feature amount calculation unit 152 calculates the representative feature amount from the search object stored in the search object temporary storage unit 170, the representative feature amount calculated from the unknown identical attribute captured image, And the attribute information of an unknown identical attribute picked-up image is output to the cell classification | category part 160. FIG. In other words, the characteristic object extraction / representative feature quantity calculation unit 152 generates a virtual object (hereinafter, referred to as “virtual search object”) having the representative feature quantity as its own morphological feature quantity. The attribute information and the morphological feature amount (that is, the representative feature amount) of the target search object are output to the cell classification unit 160.

  The classification model storage unit 180 stores an unknown attribute captured image or a classification model for classifying the cells in the unknown attribute captured image for each attribute based on the morphological characteristics of the cells. For example, as shown in FIG. 7, the classification model storage unit 180 may store a classification model that classifies the attributes of cell objects in stages according to a predetermined classification standard.

  The cell catalog storage unit 190 stores a cell catalog as shown in FIG. In the cell catalog shown in FIG. 16, for example, classification destination ID “N0001”, attribute information (name “A”, culture condition “OO”, etc.), and morphological feature (area “27”, length “10.5”). Etc.) in association with each other. Note that the cell catalog may further store an image of the cell object (for example, binarized data representing the cell object).

  The cell classification unit 160 inputs the morphological feature amount of the cell object recognized from the unknown attribute captured image by the cell object recognition unit 120 or the feature amount calculated from the morphological feature amount into the classification model, thereby making the unknown The cells in the attribute captured image or the unknown attribute captured image are classified.

  For example, when an unknown heterogeneous attribute captured image is input to the cell classification device 3, the cell classifying unit 160 extracts features extracted from cell objects (that is, search objects) recognized from the unknown heterogeneous attribute captured image. Based on the morphological feature amount of the target object (that is, the characteristic search object), the cells in the unknown heterogeneous attribute captured image are classified. Specifically, when the cell classification unit 160 acquires attribute information and morphological feature amounts of a plurality of characteristic search objects from the characteristic object extraction / representative feature amount calculation unit 152, the plurality of characteristic search objects The attribute information and the morphological feature amount are input into the classification model, and the individual characteristic search objects are classified.

  For example, when an unknown identical attribute captured image is input to the cell classification device 3, the cell classification unit 160 uses the morphological feature amount of the cell object (that is, the search object) recognized from the unknown identical attribute captured image. Based on the calculated feature quantity (ie, representative feature quantity), the unknown identical-attribute captured images are classified. Specifically, when the cell classification unit 160 acquires the attribute information and the morphological feature amount (that is, the representative feature amount) of the virtual search object from the characteristic object extraction / representative feature amount calculation unit 152, The attribute information and the morphological feature amount of the virtual search object are input to the classification model, and the unknown identical attribute captured image is classified. That is, the cell classification unit 160 sets the classification destination of the virtual search object as the classification destination of the unknown identical-attribute captured image.

  When classifying the unknown attribute captured image or the cells in the unknown attribute captured image, the cell classification unit 160 outputs the classification result to the input / output unit 110. Specifically, when the individual characteristic search objects obtained from the unknown heterogeneous attribute captured images are classified, the cell classification unit 160 is set to at least the branch destination of the individual characteristic search objects. The classification destination ID is output to the input / output unit 110. For example, in addition to the classification destination IDs of the individual characteristic search objects, the cell classification unit 160 can select either attribute information associated with each classification destination ID or the morphological feature amount of each characteristic search object. Either or both may be output to the input / output unit 110.

  Note that when the individual characteristic search objects are classified, the cell classification unit 160 associates the individual characteristic search objects with the classification destination ID indicating the classification destination of the individual characteristic search objects, and the morphological feature amounts of the individual characteristic search objects. May be added to the cell catalog. In addition, when the characteristic search object is classified, the cell classification unit 160 is a classification result in addition to information obtained from the classification model as a classification result (for example, classification destination ID, attribute information for each classification destination ID). The attribute information and morphological feature amount corresponding to each classification destination ID may be acquired from the cell catalog storage unit 190 and output to the input / output unit 110.

  When classifying the unknown identical attribute captured image based on the virtual search object obtained from the unknown identical attribute captured image, the cell classification unit 160 is set to at least the branch to which the unknown identical attribute captured image is classified. The classification destination ID is output to the input / output unit 110. For example, the cell classification unit 160 adds the attribute information associated with the classification destination ID or the morphological feature amount of the virtual search object (that is, the representative feature) in addition to the classification destination ID of the unknown identical attribute captured image. Any one or both of (quantity) may be output to the input / output unit 110.

  In addition, when the cell classification unit 160 classifies the unknown identical attribute captured image, the cell classification unit 160 associates the unknown attribute captured image with the classification destination ID indicating the classification destination of the unknown attribute captured image, and the morphological feature of the virtual search object used for the classification The quantity may be added to the cell catalog. In addition, when classifying an unknown identical-attribute captured image, the cell classification unit 160 adds attribute information and morphological features corresponding to the classification destination ID that is the classification result in addition to the information obtained from the classification model as the classification result. It may be acquired from the cell catalog storage unit 190 and output to the input / output unit 110.

  The relationship between the characteristic object extraction / representative feature amount calculation unit 152 extraction of the characteristic search object and the cell classification unit 160 classification may be realized as follows, for example.

  After storing the morphological feature amount of the search object in the search object temporary storage unit 170, the cell object feature amount calculation unit 126 notifies the cell classification unit 160 that the morphological feature amount has been stored. When the cell classification unit 160 obtains the notification that the morphological feature amount is stored from the cell object feature amount calculation unit 126, the cell classification unit 160 requests the characteristic object extraction / representative feature amount calculation unit 152 to perform extraction or calculation.

  The feature object extraction / representative feature amount calculation unit 152 refers to the image type information stored in the search object temporary storage unit 170 when extraction or calculation is requested from the cell classification unit 160, and extracts the search object. It is determined whether the original is an unknown different-attribute captured image or an unknown identical-attribute captured image. Specifically, the characteristic object extraction / representative feature amount calculation unit 152 indicates that the image type information stored in the search object temporary storage unit 170 indicates that the search object extraction source is an unknown heterogeneous attribute captured image. If the search object extraction source is an unknown different-attribute captured image, the search object extraction source is image type information indicating that the search object extraction source is an unknown identical-attribute captured image. Determines that the extraction source of the search object is an unknown identical-attribute captured image.

  When the characteristic object extraction / representative feature quantity calculation unit 152 determines that the search object extraction source is an unknown heterogeneous attribute captured image, the characteristic search object is extracted from the search object stored in the search object temporary storage unit 170. Are extracted, the classification is requested, and the attribute information and the morphological feature amount of each characteristic search object are output to the cell classification unit 160.

  When the cell classification unit 160 obtains the attribute information and the morphological feature amount of each characteristic search object from the characteristic object extraction / representative feature amount calculation unit 152 together with the classification request, the cell classification unit 160 Are put into a classification model to classify individual characteristic search objects.

  On the other hand, when the characteristic object extraction / representative feature quantity calculation unit 152 determines that the extraction source of the search object is an unknown identical-attribute captured image, the characteristic object extraction / representative feature value calculation unit 152 determines whether the search object is stored in the search object temporary storage unit 170 from A search object is generated, classification is requested, and attribute information and morphological feature of the virtual search object are output to the cell classification unit 160.

  The cell classification unit 160 acquires the attribute information and the morphological feature amount of the virtual search object from the characteristic object extraction / representative feature amount calculation unit 152 together with the classification request, and the cell classification unit 160 acquires the virtual search object. Is input to the classification model, and the unknown identical attribute captured image is classified.

  Hereinafter, the operation of the cell classification device 3 will be described using a flowchart. FIG. 17 is a flowchart showing an example of the operation of the cell classification device 3. Further, it is assumed that nothing is stored in the search object temporary storage unit 170 at the start of the flowchart shown in FIG.

  In FIG. 17, the input / output unit 110 acquires image type information from the outside together with the unknown attribute captured image (step S212). The input / output unit 110 outputs the unknown attribute captured image and image type information acquired from the outside to the binarization processing unit 122 of the cell object recognition unit 120.

  The binarization processing unit 122 binarizes the unknown attribute captured image acquired from the input / output unit 110 using a predetermined threshold (step S214). The binarization processing unit 122 outputs the binarized data and the image type information to the noise estimation unit 124. The noise estimation unit 124 acquires the binarized data from the binarization processing unit 122, and estimates the noise object from all the objects obtained from the acquired binarized data (step S220). The noise estimation unit 124 that has estimated the noise object outputs estimation result information to the cell object feature amount calculation unit 126 together with the binarized data and the image type information. The details of the process of step S220 by the noise estimation unit 124 are the same as the details of the process of step S120 by the noise estimation unit 24 of the classification model generation device 1 according to the first embodiment (FIG. 12).

  The cell object feature amount calculation unit 126 that has acquired the estimation result information together with the binarized data and the image type information from the noise estimation unit 124, based on the binarization data and the estimation result information acquired from the noise estimation unit 124, the 2 A noise object is removed from all objects obtained from the valuation data (step S230). In other words, the cell object feature quantity calculation unit 126 recognizes individual cell objects other than the noise object from the binarized data acquired from the noise estimation unit 124 (step S230).

  Next, the cell object feature value calculation unit 126 calculates the morphological feature value of each recognized search object (step S232). The cell object feature amount calculation unit 126 that has calculated the morphological feature amount of each search object stores the image type information and the morphological feature amount of each search object in the search object temporary storage unit 170 (step S234). . In addition, the cell object feature amount calculation unit 126 notifies the cell classification unit 160 that the morphological feature amount is stored. The cell classification unit 160 that has acquired the notification that the morphological feature amount is stored requests the feature object extraction / representative feature amount calculation unit 152 to perform extraction or calculation.

  The characteristic object extraction / representative feature amount calculation unit 152 requested to extract or calculate from the cell classification unit 160 determines whether or not the unknown attribute captured image acquired in step S212 is an unknown heterogeneous attribute captured image ( Step S280).

  When the characteristic object extraction / representative feature amount calculation unit 152 determines that the unknown attribute captured image acquired in step S212 is an unknown heterogeneous attribute captured image (step S280: Yes), the characteristic object extraction / representative feature amount calculation unit 152 stores the extracted in the extraction condition storage unit 174. The characteristic search object is extracted from the search object stored in the search object temporary storage unit 170 in accordance with the extraction condition (step 282). The characteristic object extraction / representative feature quantity calculation unit 152 that has extracted the characteristic search object requests classification, and outputs attribute information and morphological feature quantity of each characteristic search object to the cell classification unit 160. The cell classification unit 160 that obtains the attribute information and the morphological feature amount of each characteristic search object from the characteristic object extraction / representative feature amount calculation unit 152 together with the request for the classification, classifies each characteristic search object as a classification model. And classifying the individual characteristic search objects in the unknown different-attribute captured image (step S284).

  On the other hand, the characteristic object extraction / representative feature amount calculation unit 152 determines that the unknown attribute captured image acquired in step S212 is not an unknown heterogeneous attribute captured image (step S280: No), that is, acquired in step S212. If it is determined that the unknown attribute captured image is an unknown identical attribute captured image, a virtual search object is generated from the search object stored in the search object temporary storage unit 170 (step 286). The characteristic object extraction / representative feature quantity calculation unit 152 that has generated the virtual search object outputs a request for classification and the attribute information and the morphological feature quantity of the virtual search object to the cell classification unit 160. The cell classification unit 160 that has acquired the attribute information and morphological feature amount of the virtual search object from the characteristic object extraction / representative feature amount calculation unit 152 together with the request for classification, inputs the virtual search object to the classification model, The unknown identical attribute captured images are classified (step S288).

  Subsequent to step S284 or step S288, the cell classification unit 160 outputs the classification result to the input / output unit 110 (step S290). Specifically, when classifying individual characteristic search objects in step S284, the cell classification unit 160 corresponds to, for example, a classification destination ID indicating a classification destination of each characteristic search object, and each classification destination ID. The attached attribute information and the morphological feature amount of each characteristic search object are output to the input / output unit 110. When the cell classification unit 160 classifies the unknown identical attribute captured image in step S288, for example, the classification destination ID indicating the classification destination of the unknown identical attribute captured image, the attribute information associated with the classification destination ID, for example. The morphological feature amount of the virtual search object is output to the input / output unit 110.

  Next, the cell classification unit 160 deletes the search object stored in the search object temporary storage unit 170 (step S292), and the flowchart shown in FIG. 17 ends. For example, the cell classification unit 160 stores the search object temporary storage unit 170 by requesting the cell object feature amount calculation unit 126 to delete the search object stored in the search object temporary storage unit. Delete the search object that has been deleted.

(Fourth embodiment)
The fourth embodiment of the present invention will be described below with reference to the drawings. FIG. 18 is an example of a functional block diagram of the cell classification device 4 according to the fourth embodiment of the present invention. FIG. 19 is an example of information stored in the search object temporary storage unit 270.

  As shown in FIG. 17, the cell classification device 4 includes an input / output unit 210, a cell object recognition unit 220, a characteristic object extraction / representative feature amount calculation unit 252, a candidate classification model formation unit 254, a classification model determination / formation unit. 256, a cell classification unit 260, a search object temporary storage unit 270, a cell object storage unit 272, an extraction condition storage unit 274, a candidate classification model storage unit 276, a classification model storage unit 280, and a cell catalog storage unit 290. The cell object recognition unit 220 includes a binarization processing unit 222, a noise estimation unit 224, and a cell object feature amount calculation unit 226.

  The input / output unit 210 acquires a captured image and image type information from the outside. Specifically, the input / output unit 210, together with attribute information (or attribute information and imaging condition information), as well as the known identical attributes, is the same as the input / output unit 10 of the classification model generation devices 1 and 2 according to the first embodiment. Image type information indicating that the image is a captured image and a known image having the same attribute is acquired from the outside. Also, the input / output unit 210 is similar to the input / output unit 110 of the cell classification device 3 according to the third embodiment described above, image type information indicating that it is an unknown heterogeneous attribute captured image and an unknown heterogeneous attribute captured image, or Image type information indicating that it is an unknown identical attribute captured image and an unknown identical attribute captured image is acquired from the outside. The input / output unit 210 outputs information acquired from the outside to the binarization processing unit 222.

  Further, the input / output unit 210 may acquire the classification destination range designation information from the outside together with the unknown attribute captured image (unknown different attribute captured image or unknown identical attribute captured image), and output the classification destination range designation information to the binarization processing unit 222. . The classification destination range designation information is information that designates a classification destination range of an unknown attribute captured image or a classification destination range of cells in an unknown attribute captured image.

  Similar to the cell object recognition unit 20 of the classification model generation device 1 according to the first and second embodiments, the cell object recognition unit 220 recognizes an imaging region of each cell in a known identical attribute captured image as a cell object. . In addition, the cell object recognition unit 220 recognizes each cell object in the unknown attribute captured image as a search object, like the cell object recognition unit 120 of the cell classification device 3 according to the third embodiment.

  That is, the binarization processing unit 222 binarizes a known identical attribute captured image with a predetermined threshold as in the binarization processing unit 22 of the classification model generation apparatuses 1 and 2 according to the first and second embodiments. Process. Similarly to the binarization processing unit 122 of the cell classification device 3 according to the third embodiment, the binarization processing unit 222 binarizes the unknown attribute captured image with a predetermined threshold. The binarization processing unit 222 outputs the binarized data to the noise estimation unit 124 together with the information acquired from the input / output unit 210.

  The noise estimation unit 224 estimates a noise object from all the objects in the known identical attribute captured image, like the noise estimation unit 24 of the classification model generation apparatuses 1 and 2 according to the first and second embodiments. Moreover, the noise estimation part 224 estimates a noise object from all the objects in an unknown attribute picked-up image similarly to the noise estimation part 124 of the cell classification device 3 by said 3rd Embodiment. The noise estimation unit 224 outputs the estimation result information to the noise estimation unit 124 together with the information acquired from the binarization processing unit 222.

  The cell object feature amount calculation unit 226 is similar to the cell object feature amount calculation unit 26 of the classification model generation apparatuses 1 and 2 according to the first and second embodiments described above, and each cell object form in the known identical attribute captured image. The characteristic feature amount is calculated. The cell object feature amount calculation unit 226 that has calculated the morphological feature amount of each cell object stores the morphological feature amount of each cell object in the cell object in association with the attribute information (or attribute information and imaging condition information). Store in the unit 272.

  In addition, the cell object feature quantity calculation unit 226 is similar to the cell object feature quantity calculation unit 126 of the cell classification device 3 according to the third embodiment described above, and each cell object (that is, search object) in the unknown attribute captured image. The morphological feature amount of is calculated. The cell object feature value calculation unit 226 that calculates the morphological feature value of each search object stores the morphological feature value of each search object in the search object temporary storage unit 270 in association with the image type information. In addition, the cell object feature amount calculation unit 226 stores the classification destination range designation information in the search object temporary storage unit 270 when the classification destination range designation information is acquired from the noise estimation unit 224.

  The search object temporary storage unit 270 stores information related to the search object. Specifically, as shown in FIGS. 18A and 18B, the search object temporary storage unit 270 associates the morphological feature amount of each search object with the image type information and the classification destination range designation information. Remember. The initial value of the item “image type information” in the search object temporary storage unit 270 is the same as that of the search object temporary storage unit 170 of the cell classification device 3 according to the third embodiment. The initial value of the item “classification destination range designation information” in the search object temporary storage unit 270 is classification destination range designation information indicating that no classification range is designated. Therefore, when the cell object feature quantity calculation unit 226 has not acquired the classification destination range designation information from the noise estimation unit 224, the classification destination indicating that no classification range is designated in the item “classification destination range designation information”. Range designation information is stored.

  The cell object storage unit 272 is the same as the cell object storage unit 72 of the classification model generation apparatuses 1 and 2 according to the first and second embodiments. The extraction condition storage unit 274 is the same as the extraction condition storage unit 74 of the classification model generation device 1 according to the first embodiment. The extraction condition storage unit 274 extracts an extraction condition for extracting a characteristic object from the cell object storage unit 272 (that is, an extraction condition for extracting a characteristic object used when generating a classification model), and a search object temporary storage. The extraction condition for extracting the characteristic search object from the unit 270 (that is, the extraction condition for extracting the characteristic search object to be classified) may be stored separately.

  The candidate classification model storage unit 276 is the same as the candidate classification model storage unit 76 of the classification model generation device 1 according to the first embodiment. The classification model storage unit 280 is the same as the classification model storage unit 80 of the classification model generation apparatuses 1 and 2 according to the first and second embodiments. The cell catalog storage unit 290 is the same as the cell catalog storage unit 190 of the cell classification device 3 according to the third embodiment.

  The characteristic object extraction / representative feature quantity calculation unit 252 includes the characteristic object extraction unit 32 of the classification model generation device 1 according to the first embodiment and the classification model generation device 2 according to the second embodiment. The representative feature amount calculation unit 42 is provided.

  Specifically, the characteristic object extraction / representative feature amount calculation unit 252 stores the characteristic object in the cell object storage unit 272 according to the extraction condition stored in the extraction condition storage unit 274 when the characteristic object is to be extracted. A characteristic object is extracted from the stored cell objects. However, when the classification object range designation information indicating that the classification range is designated is stored in the search object temporary storage unit 270, the characteristic object extraction / representative feature amount calculation unit 252 includes the cell object storage unit 272. A characteristic object is extracted from the cell objects in the specified classification range among the cell objects stored in. When the characteristic object extraction / representative feature quantity calculation unit 252 extracts a characteristic object from the cell objects stored in the cell object storage unit 272, the attribute information and the morphological feature quantity of each characteristic object are extracted. Is output to the candidate classification model forming unit 254.

  Further, the characteristic object extraction / representative feature amount calculation unit 252 calculates a representative feature amount from the cell object stored in the cell object storage unit 272 when the representative feature amount is to be calculated. In other words, the characteristic object extraction / representative characteristic amount calculation unit 252 generates a virtual object of each known identical attribute captured image. However, when the classification object range designation information indicating that the classification range is designated is stored in the search object temporary storage unit 270, the characteristic object extraction / representative feature amount calculation unit 252 includes the cell object storage unit 272. The representative feature amount is calculated by using the cell objects in the specified classification range among the cell objects stored in. When the characteristic object extraction / representative feature amount calculation unit 252 generates a virtual object of each known identical attribute captured image, the classification model determination / formation unit 256 determines the attribute information and the morphological feature amount of each virtual object. Output to.

  Further, the characteristic object extraction / representative feature quantity calculation unit 252 stores the image type information indicating that the search object extraction source is an unknown heterogeneous attribute captured image in the search object temporary storage unit 270. Extracts a characteristic search object from the search objects stored in the search object temporary storage unit 270 in accordance with the extraction conditions stored in the extraction condition storage unit 274. When the characteristic object extraction / representative characteristic amount calculation unit 252 extracts characteristic search objects from the search objects stored in the search object temporary storage unit 170, the attribute information and the morphological features of each characteristic search object are extracted. The amount is output to the cell classification unit 260.

  Further, the characteristic object extraction / representative feature amount calculation unit 252 stores, when the search object temporary storage unit 270 stores image type information indicating that the search object extraction source is an unknown same-attribute captured image. Calculates a representative feature amount from the search object stored in the search object temporary storage unit 270. In other words, the characteristic object extraction / representative characteristic amount calculation unit 252 generates a virtual search object for an unknown identical attribute captured image. The characteristic object extraction / representative feature amount calculation unit 252 outputs the attribute information and the morphological feature amount of the virtual search object to the cell classification unit 260 when the virtual search object of the unknown identical attribute captured image is generated.

  The candidate classification model formation unit 254 is the same as the candidate classification model formation unit 34 of the classification model generation device 1 according to the first embodiment described above. A candidate classification model is formed (generated) using the attribute information and the morphological feature amount.

  The classification model determination / formation unit 256 includes the classification model determination unit 36 of the classification model generation device 1 according to the first embodiment and the classification model formation unit 46 of the classification model generation device 2 according to the second embodiment. It has the function of.

  Specifically, the classification model determination / formation unit 256, when a plurality of candidate classification models are formed by the candidate classification model formation unit 254, a plurality of candidate classification models stored in the candidate classification model storage unit 276. A classification model is determined from the above and stored in the classification model storage unit 280.

  In addition, when the classification model determination / formation unit 256 acquires the attribute information and the morphological feature amount of a plurality of virtual objects for each known identical attribute captured image from the characteristic object extraction / representative feature amount calculation unit 252. The classification model is generated (formed) using the attribute information and the morphological feature amount of the plurality of virtual objects.

  Similar to the cell classification unit 160 of the cell classification device 3 according to the third embodiment, the cell classification unit 260 is an unknown attribute captured image or a cell (specifically, individual characteristic captured image). Search object), and the classification result is output to the input / output unit 210.

  Specifically, when the cell classification unit 260 acquires attribute information and morphological feature amounts of a plurality of characteristic search objects from the characteristic object extraction / representative feature amount calculation unit 252, the plurality of characteristic features are extracted. The attribute information and the morphological feature amount of the search object are input to the classification model, and the individual characteristic search object is classified.

  In addition, when the cell classification unit 260 acquires the attribute information and the morphological feature amount of the virtual search object from the characteristic object extraction / representative feature amount calculation unit 252, the attribute information and the form of the virtual search object The characteristic feature amount is input to the classification model, and the unknown identical attribute captured image is classified.

  Hereinafter, the operation of the cell classification device 4 will be described using a flowchart. FIG. 20 is a flowchart showing an example of the operation of the cell classification device 4. Note that, at the start of the flowchart shown in FIG. 20, it is assumed that a plurality of cell objects respectively recognized from a plurality of known identical attribute captured images are stored in the cell object storage unit 272. Further, it is assumed that nothing is stored in the search object temporary storage unit 270 at the start of the flowchart shown in FIG.

  In FIG. 20, the input / output unit 210 obtains an unknown attribute captured image together with image type information and classification destination range designation information from the outside (step S312), and outputs it to the binarization processing unit 222. The binarization processing unit 222 binarizes the unknown attribute captured image with a predetermined threshold (step S314). The binarization processing unit 222 outputs the binarized data, the image type information, and the classification destination range designation information to the noise estimation unit 224.

  The noise estimation unit 224 estimates a noise object among all objects obtained from the binarized data acquired from the binarization processing unit 222 (step S320). The noise estimation unit 224 that estimated the noise object outputs the estimation result information to the cell object feature amount calculation unit 226 together with the binarized data, the image type information, and the classification destination range designation information. The details of the process of step S320 by the noise estimation unit 224 are the same as the details of the process of step S120 by the noise estimation unit 24 of the classification model generation device 1 according to the first embodiment (FIG. 12).

  The cell object feature amount calculation unit 226 that has acquired the estimation result information together with the binarized data, the image type information, and the classification destination range designation information from the noise estimation unit 224 has the binarized data and the estimation acquired from the noise estimation unit 224. Based on the result information, the noise object is removed from all objects obtained from the binarized data (step S330). In other words, the cell object feature quantity calculation unit 226 recognizes individual cell objects other than the noise object from the binarized data acquired from the noise estimation unit 224 (step S330).

  Next, the cell object feature value calculation unit 226 calculates the morphological feature value of each recognized search object (step S332). The cell object feature amount calculation unit 226 that has calculated the morphological feature amount of each search object stores the image type information, the classification destination range designation information, and the morphological feature amount of each search object in the search object temporary storage unit 270. Store (step S334). In addition, the cell object feature amount calculation unit 226 notifies the cell classification unit 260 that the morphological feature amount of the search object has been stored. The cell classification unit 260 that has acquired the notification that the morphological feature amount of the search object has been stored requests the feature object extraction / representative feature amount calculation unit 252 to perform extraction or calculation.

  The characteristic object extraction / representative feature quantity calculation unit 252 requested to extract or calculate from the cell classification unit 260 determines whether or not the unknown attribute captured image acquired in step S312 is an unknown heterogeneous attribute captured image ( Step S340). The characteristic object extraction / representative feature quantity calculation unit 252 is unknown if the search object temporary storage unit 270 stores image type information indicating that the search object extraction source is an unknown heterogeneous attribute captured image. It is determined that the image is a heterogeneous attribute captured image.

  When the characteristic object extraction / representative feature amount calculation unit 252 determines that the unknown attribute captured image acquired in step S312 is an unknown heterogeneous attribute captured image (step S340: Yes), the characteristic object extraction / representative feature amount calculation unit 252 stores the extracted attribute attribute image in the extraction condition storage unit 274. In accordance with the extraction conditions, a characteristic object is extracted from the cell objects stored in the cell object storage unit 272 (step S342). The feature object extraction / representative feature amount calculation unit 252 that has extracted the feature object outputs the attribute information and the morphological feature amount of each feature object to the candidate classification model formation unit 254.

  The candidate classification model forming unit 254 that has acquired the attribute information and the morphological feature amount of each characteristic object forms a candidate classification model based on the attribute information and the morphological feature amount of each characteristic object. The model is stored in the candidate classification model storage unit 276 (step S344). Next, the candidate classification model forming unit 254 determines whether all candidate classification models have been formed (step S346). For example, the candidate classification model forming unit 254 requests extraction from the characteristic object extraction / representative feature amount calculation unit 252 and obtains notification from the characteristic object extraction / representative feature amount calculation unit 252 that extraction has been completed. If so, it is determined that all candidate classification models have been formed. If the candidate classification model forming unit 254 determines that not all candidate classification models have been formed (step S346: No), the process returns to step S342. If it is determined that all candidate classification models have been formed (step S346: Yes), the candidate classification model forming unit 254 requests the classification model determination / formation unit 256 to determine a classification model.

  The classification model determination / formation unit 256 requested to determine the classification model determines a classification model from among a plurality of candidate classification models, and stores the classification model in the classification model storage unit 280 (step S350). If the previous classification model is stored in the classification model storage unit 280, the classification model determination / formation unit 256 overwrites it with the classification model determined this time. The classification model determination / formation unit 256 that stores the classification model in the classification model storage unit 280 requests the characteristic object extraction / representative feature amount calculation unit 252 to perform extraction.

  The characteristic object extraction / representative feature quantity calculation unit 252 requested to be extracted by the classification model determination / formation unit 256 is characterized by the search object temporary storage unit 270 according to the extraction conditions stored in the extraction condition storage unit 274. A search object is extracted (step 382). The characteristic object extraction / representative feature quantity calculation unit 252 that has extracted the characteristic search object requests the cell classification unit 260 to classify and outputs the attribute information and the morphological feature quantity of the characteristic search object. The cell classification unit 260 that has acquired the attribute information and the morphological feature amount of the characteristic search object together with the request for classification from the characteristic object extraction / representative feature amount calculation unit 252, uses each characteristic search object as a classification model. And classifying the individual characteristic search objects in the unknown different-attribute captured image (step S384).

  On the other hand, when the characteristic object extraction / representative feature amount calculation unit 252 determines that the unknown attribute captured image acquired in step S312 is not an unknown heterogeneous attribute captured image (step S340: NO), the characteristic object extraction / representative feature amount calculation unit 252 stores it in the cell object storage unit 272. The representative feature amount is calculated from the cell object that has been set (step S352). In other words, the characteristic object extraction / representative characteristic amount calculation unit 252 generates a virtual object from the cell objects stored in the cell object storage unit 272 (step S352). The characteristic object extraction / representative feature amount calculation unit 252 that calculates the representative feature amount from each of the known identical attribute captured images sends the attribute information and the morphological feature amount of each virtual object to the classification model determination / formation unit 256. Output.

  The classification model determination / formation unit 256 that has acquired the attribute information and the morphological feature amount of each virtual object forms a classification model based on the attribute information and the morphological feature amount of each virtual object. Is stored in the classification model storage unit 280 (step S360). If the previous classification model is stored in the classification model storage unit 280, the classification model determination / formation unit 256 overwrites it with the classification model determined this time. The classification model determination / formation unit 256 that stores the classification model in the classification model storage unit 280 requests the characteristic object extraction / representative feature amount calculation unit 252 to calculate.

  The characteristic object extraction / representative feature amount calculation unit 252 requested to calculate by the classification model determination / formation unit 256 calculates a representative feature amount from the search object stored in the search object temporary storage unit 270 (step S386). That is, the characteristic object extraction / representative characteristic amount calculation unit 252 generates a virtual search object from the search object stored in the search object temporary storage unit 270 (step S386). The characteristic object extraction / representative feature quantity calculation unit 252 that has calculated the representative feature quantity requests the cell classification unit 260 to classify and outputs the attribute information and the morphological feature quantity of the virtual search object. The cell classification unit 260 that has acquired the attribute information and the morphological feature amount of the virtual search object together with the classification request from the characteristic object extraction / representative feature amount calculation unit 252 inputs the virtual search object to the classification model. Then, the unknown identical attribute captured images are classified (step S388).

  Subsequent to step S384 or step S388, the cell classification unit 260 outputs the classification result to the input / output unit 210 (step S390). Next, the cell classification unit 260 deletes the search object stored in the search object temporary storage unit 270 (step S392), and the flowchart shown in FIG.

  Note that in step S342, the characteristic object extraction / representative feature amount calculation unit 252 determines that the classification destination range designation information indicating that the classification range is designated is stored in the search object temporary storage unit 270, the cell A characteristic object is extracted from the cell objects in the specified classification range among the cell objects stored in the object storage unit 272. Similarly, in step S352, the characteristic object extraction / representative characteristic amount calculation unit 252 stores classification destination range designation information indicating that a classification range is designated in the search object temporary storage unit 270. The representative feature amount is calculated using the cell objects in the designated classification range among the cell objects stored in the cell object storage unit 272.

Note that, in step S382, the characteristic object extraction / representative characteristic amount calculation unit 252 uses a classification object as a classification model among the extracted patterns when the characteristic object is extracted from the cell objects stored in the cell object storage unit 272. It is preferable to extract the characteristic search object from the search object temporary storage unit 270 according to the extraction pattern when the determined candidate classification model is extracted. For example, in the example shown in FIGS. 4 and 5, the characteristic object extraction / representative characteristic amount calculation unit 252 has an area from an average value to an average value among (N ₁ + N ₂ + N ₃ ) cell objects. It is within the range of 25% larger value (extraction condition 1), and the length is within the range of the average value to 25% larger than the average value (extraction condition 3), and the width is the average value. When a cell object that is within a range of 25% larger than the average value (extraction condition 5) is extracted as a characteristic object, and finally a candidate classification model formed from the characteristic object is determined as a classification model The search object temporary storage unit 270 extracts the search object that is the extraction condition 1, the extraction condition 3, and the extraction condition 5 as a characteristic search object. . That is, in short, the characteristic object extraction / representative characteristic amount calculation unit 252 extracts characteristic search objects using the same extraction pattern as the extraction pattern reflected in the classification model.

  There are various methods for extracting a characteristic search object by using the same extraction pattern as the extraction pattern reflected in the classification model. For example, the characteristic object extraction / representative feature amount calculation unit 252 performs the extraction pattern. Is output to the candidate classification model formation unit 254, the candidate classification model formation unit 254 stores the information indicating the extraction pattern in the candidate classification model storage unit 276, and the classification model determination / formation unit 256 outputs the extraction pattern. Information to be stored is stored in the classification model storage unit 280. After that, the characteristic object extraction / representative feature quantity calculation unit 252 refers to the classification model storage unit 280 when extracting the characteristic search object, and extracts when the candidate classification model determined as the classification model is extracted. Identify the pattern.

  Similarly, in step S386, the characteristic object extraction / representative characteristic amount calculation unit 252 stores the search object temporarily according to the calculation method when the virtual object is generated from the cell object stored in the cell object storage unit 272. It is preferable to generate a virtual search object from the search object stored in the unit 270. For example, the feature object extraction / representative feature amount calculation unit 252 calculates the median value of each morphological feature amount of a plurality of cell objects extracted from the known identical attribute captured image as a representative feature amount, When a virtual object having the median feature value as its morphological feature value is generated, the median value of each morphological feature value of the search object stored in the search object temporary storage unit 270 is a representative feature. As a quantity, a virtual search object is generated in which the median value of each morphological feature quantity is its own morphological feature quantity. That is, in short, the characteristic object extraction / representative feature quantity calculation unit 252 generates a virtual search object by the same calculation method as the representative feature quantity calculation method reflected in the classification model.

  There are various methods for generating a virtual search object by the same calculation method as the representative feature amount reflected in the classification model. For example, a feature object extraction / representative feature amount calculation unit 252 outputs information indicating the calculation method when the virtual object is generated to the classification model determination / formation unit 256, and the classification model determination / formation unit 256 transmits the information indicating the calculation method to the classification model storage unit 280. Remember. After that, the characteristic object extraction / representative feature quantity calculation unit 252 refers to the classification model storage unit 280 when generating the virtual search object, and specifies the calculation method when the virtual object is generated.

  As described above, the classification model generation device 1 according to the first embodiment, the classification model generation device 2 according to the second embodiment, the cell classification device 3 according to the third embodiment, and the cell classification device 4 according to the fourth embodiment. As described above, according to the cell classification device 3, by using a classification model that classifies the attributes of the cell object according to a predetermined classification standard, the cell type, The state etc. can be confirmed. Moreover, according to the classification model generation apparatuses 1 and 2, the classification model used in the cell classification apparatus 3 can be easily generated. Furthermore, according to the cell classification device 4, since the latest classification model is generated every time the cell is confirmed, the type and state of the cell can be confirmed with higher accuracy.

  Moreover, since the noise object is accurately removed by the noise estimation units included in the classification model generation devices 1 and 2 and the cell classification devices 3 and 4, the cell object can be extracted with high accuracy.

  In addition, although the example of the classification model classified in steps was demonstrated as the classification model which the classification model production | generation apparatus 1 produces | generates, you may produce | generate the classification model classified at a stretch in one step. The same applies to the classification model generated by the classification model generation device 2, the classification model used by the cell classification device 3, and the classification model generated and used by the cell classification device 4. In addition, the classification model according to each embodiment includes one that determines whether or not an attribute is at most.

  In the cell classification device 3 according to the third embodiment, the characteristic object extraction / representative characteristic amount calculation unit 152 extracts a characteristic search object from the search objects stored in the search object temporary storage unit 170, and Although the attribute information and the morphological feature amount of each characteristic search object are output to the cell classification unit 160, the characteristic object extraction / representative feature amount calculation unit 152 performs a search that is not a characteristic search object and a characteristic search object. In a manner that can distinguish an object (referred to as a non-characteristic search object) (for example, with a label), in addition to the characteristic search object, a non-characteristic search object may be output to the cell classification unit 160.

  The cell classification unit 160 that has acquired the non-characteristic search object in addition to the characteristic search object also inputs the non-characteristic search object into the classification model, as well as the characteristic search object. The classification result of the characteristic search object may be output, and the classification result of each non-characteristic search object may be output together with the information that the classification accuracy is not high. Further, the cell classification unit 160 that has acquired the non-characteristic search object in addition to the characteristic search object may output “non-classifiable” for each non-characteristic search object. The same applies to the characteristic object extraction / representative feature amount calculation unit 252 and the cell classification unit 260 of the cell classification device 4 of the fourth embodiment.

  In the fourth embodiment, the characteristic object extraction / representative feature amount calculation unit 252 of the cell classification device 4 performs the classification model from the cell objects stored in the cell object storage unit 272 when generating the classification model. A virtual object is generated as an object serving as a basis for the search, and a virtual search object to be classified is generated from a search object stored in the search object temporary storage unit 270 at the time of classification. The characteristic object extraction / representative feature quantity calculation unit 252 generates all the cell objects stored in the cell object storage unit 272 without generating a virtual object when generating the classification model. In addition, the virtual search object is Without growth, all the search objects stored in the retrieval object temporary storage unit 270 (i.e., all search objects obtained from an unknown identity attribute image) may be a classification target. In the above case, for example, the cell classification unit 260 inputs all the search objects stored in the search object temporary storage unit 270 into the classification model, and classifies the branch with the largest number of search objects as the classification of the unknown identical attribute image. It may be the destination.

  In the first embodiment, the cell object storage unit 72 of the classification model generation device 1 has been described as an example of storing morphological feature quantities having no temporal elements. However, the input / output unit 10 captures images at different times. The morphological feature amount having temporal elements may be stored in the cell object storage unit 72 by inputting the plurality of known identical attribute captured images. For example, the input / output unit 10 captures a known identical attribute captured image a captured at a first time (for example, the eighth hour of culture) for a cell having a certain attribute, and a second time (for example, the culture 16) for the cell. A known identical attribute captured image b captured at time) is input. The cell object feature amount calculation unit 26 calculates the morphological feature amount of the cell object of the known identical attribute captured image a, and stores the cell object storage unit 72 in the cell object storage unit 72 from the first time to the second time. The temporal change amount of the morphological feature amount is stored. An example of the temporal change amount of the morphological feature amount is difference information for each morphological feature amount such as an area temporal change amount, a length temporal change amount, and a width change amount.

  Of course, when the morphological feature quantity having a temporal element is stored in the cell object storage unit 72, the candidate classification model generated by the candidate classification model forming unit 34 and the classification model determining unit 36 are The classification model to be determined and stored in the classification model storage unit 80 has a temporal element. For example, the rule of each stage in the classification model has a morphological element having a temporal component such that “the amount of change in cell length from the first time to the second time is a value“ 1 ”or less”. In addition, the extraction conditions stored in the extraction condition storage unit 74, the evaluation by the classification model determination unit 36, and the like also correspond to the morphological feature amounts having temporal elements.

  Similarly, in the classification model generation device 2 of the second embodiment, the cell classification device 3 of the third embodiment, and the cell classification device 4 of the fourth embodiment, a classification model having a temporal element is used. May be. Of course, when a classification model having a temporal element is used, classification (retrieval) is also performed based on the temporal element. Therefore, for example, the input / output unit 110 of the cell classification device 3 externally captures the unknown attribute captured image captured at the first time and the unknown attribute captured image captured at the second time when the captured image and the subject are the same. Get from.

  In the first embodiment, it has been described that the classification model generation device 1 is separate from the incubator 311. However, the function of the classification model generation device 1 may be implemented in the incubator 311. For example, a program for executing each process of the classification model generating device 1 may be stored in the storage unit 343 illustrated in FIG. 8 and executed by the CPU 342. The same applies to the classification model generation device 2 and the cell classification devices 3 and 4.

  In the third embodiment, when the function of the cell classification device 3 is implemented in the incubator 311, the culture conditions of the cells or cell groups in the culture container 319 are determined based on the classification result of the cell classification device 3. You may make it change dynamically. Thereby, a desired cell can be easily proliferated. In the fourth embodiment, the same applies to the case where the function of the cell classification device 4 is implemented in the incubator 311.

  Hereinafter, the operation of the incubator 311 having the function of the cell classification device 4 will be described using a flowchart. FIG. 21 is a flowchart showing an example of the operation of the incubator 311 having the function of the cell classification device 4. At the start of the flowchart shown in FIG. 21, it is assumed that a culture vessel 319 for culturing desired cells to be proliferated is placed in the incubator 311. Further, as described above, the cell classification device 3 included in the incubator 311 outputs attribute information including the culture condition together with the classification destination ID as the classification result.

  In FIG. 21, the incubator 311 starts cell culture (step S500). That is, the CPU 342 controls the temperature adjustment device 315a and the humidity adjustment device 315b so as to culture the cells under the set culture conditions (for example, predetermined temperature, humidity, etc.). After a predetermined period of time, the incubator 311 classifies the cells in culture (step S510). That is, the CPU 342 controls the imaging device 334 to image cells in culture, and then reads out a program for executing each process of the cell classification device 3 from the storage unit 343 and executes the read program. As shown in FIG. 17 above, the cells in culture are classified.

  Next, the incubator 311 controls whether to stop the cell culture or change the cell culture conditions based on the classification result. Specifically, first, CPU 342 determines whether or not the culture condition indicated by the classification result matches the culture condition set in step S500 (step S520). If the CPU 342 determines that the culture condition indicated by the classification result matches the set culture condition (step S520: Yes), the CPU 342 monitors whether the culture condition indicated by the classification result matches the set culture condition, or the like. (Not shown) is displayed (step S522). And it progresses to step S560 mentioned later.

  On the other hand, when the CPU 342 determines that the culture condition included in the classification result does not match the predetermined culture condition (step S520: No), the CPU 342 indicates that the culture condition indicated by the classification result does not match the set culture condition. It is displayed on a monitor or the like (not shown) (step S524), and it is determined whether or not the culture is stopped (step S530). Specifically, for example, the CPU 342 determines to stop the culture when the degree of mismatch between the culture condition indicated by the classification result and the set culture condition is very large. For example, when the difference between the temperature or the like of the culture condition indicated by the classification result and the temperature or the like of the set culture condition is equal to or greater than a predetermined first threshold, it is determined that the culture is to be stopped. Note that the CPU 342 determines that the culture is to be stopped even when there is an input from the operator to stop the culture.

  When the CPU 342 determines that the culture is to be stopped (step S530: Yes), the CPU 342 displays on the monitor or the like (not shown) that the culture is stopped (step S532), and ends the culture process. Then, the flowchart shown in FIG. 20 ends. On the other hand, when determining that the culture is not stopped (step S530: No), the CPU 342 determines whether or not to change the culture conditions (step S540). Specifically, for example, the CPU 342 determines to change the culture condition when there is a large degree of mismatch between the culture condition indicated by the classification result and the set culture condition. For example, when the difference between the temperature of the culture condition or the like indicated by the classification result and the temperature of the set culture condition is equal to or greater than a predetermined second threshold (where the first threshold is greater than the second threshold), It is judged that the culture conditions are changed.

  If the CPU 342 determines that the culture conditions are to be changed (step S540: Yes), the CPU 342 displays on the monitor or the like (not shown) that the culture conditions are to be changed (step S542), and changes the culture conditions (step S550). That is, the CPU 342 controls the temperature adjustment device 315a and the humidity adjustment device 315b so that cells are cultured under new culture conditions. Then, the process returns to step S510. Note that both the culture conditions (the culture conditions indicated by the classification result and the set culture conditions) and the new culture conditions (the culture conditions after correction) are associated with each other and stored in the storage unit 343 in advance, and the CPU 342 May acquire new culture conditions from both culture conditions. Note that the CPU 342 may change the culture condition to the set value when the operator inputs a new set value of the culture condition.

  Following step S522 or when determining that the culture conditions are not to be changed (step S540: No), the CPU 342 controls the imaging device 334 to image the cells in culture after the predetermined period has elapsed, The number of cells (that is, the number of cell objects) is counted (step S560). Note that the CPU 342 reads out a program for executing the processing of the cell object feature quantity calculation unit 126 of the cell classification device 3 capable of recognizing individual cell objects from the storage unit 343, and executes the read program to execute the number of cells. Count.

  Next, the CPU 342 determines whether or not the number of cells counted in step S560 has reached a predetermined number of cells (step S570). When the CPU 342 determines that the counted number of cells has not reached the predetermined number of cells (step S570: No), the CPU 342 returns to step S510 after a predetermined time has elapsed. On the other hand, when the CPU 342 determines that the counted number of cells has reached the predetermined number of cells (step S570: Yes), the CPU 342 displays on the monitor or the like (not shown) that the culture of the cells has been completed (step S572). . Then, the flowchart shown in FIG. 21 ends.

  As described above, as shown in the flowchart of FIG. 21, the culture conditions of the cells in the culture vessel 319 can be dynamically changed based on the classification result of the cell classification device 4, so that desired cells can be easily propagated. Will be able to.

  The classification model generation device 1 according to the first embodiment of the present invention, the classification model generation device 2 according to the second embodiment of the present invention, the cell classification device 3 according to the third embodiment of the present invention, the first of the present invention. By recording a program for executing each process of the cell classification device 4 according to the third embodiment on a computer-readable recording medium, causing the computer system to read and execute the program recorded on the recording medium, The above-described various processes related to the classification model generation devices 1 and 2 and the cell classification devices 3 and 4 may be performed. Here, the “computer system” may include an OS and hardware such as peripheral devices. Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used. The “computer-readable recording medium” means a flexible disk, a magneto-optical disk, a ROM, a writable nonvolatile memory such as a flash memory, a portable medium such as a CD-ROM, a hard disk built in a computer system, etc. This is a storage device.

  Further, the “computer-readable recording medium” means a volatile memory (for example, DRAM (Dynamic DRAM) in a computer system that becomes a server or a client when a program is transmitted through a network such as the Internet or a communication line such as a telephone line. Random Access Memory)), etc., which hold programs for a certain period of time. The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes designs and the like that do not depart from the gist of the present invention.

DESCRIPTION OF SYMBOLS 1, 2 ... Classification model production | generation apparatus 3, 4 ... Cell classification apparatus 10 ... Input / output part 20 ... Cell object recognition part 22 ... Binarization process part 24 ... Noise estimation part 26 ... Cell object feature-value calculation part 30 ... Classification model Generation unit 32 ... Characteristic object extraction unit 34 ... Candidate classification model formation unit 36 ... Classification model determination unit 40 ... Classification model generation unit 42 ... Representative feature amount calculation unit 46 ... Classification model formation unit 72 ... Cell object storage unit 74 ... Extraction condition storage unit 76 ... candidate classification model storage unit 80 ... classification model storage unit 110 ... input / output unit 120 ... cell object recognition unit 122 ... binarization processing unit 124 ... noise estimation unit 126 ... cell object feature amount calculation unit 152 ... Feature object extraction / representative feature amount calculation unit 160 ... cell classification unit 170 ... search object temporary storage unit 174 ... extraction condition storage 180 ... classification model storage unit 190 ... cell catalog storage unit 210 ... input / output unit 220 ... cell object recognition unit 222 ... binarization processing unit 224 ... noise estimation unit 226 ... cell object feature amount calculation unit 252 ... characteristic object extraction Representative feature amount calculation unit 254 ... candidate classification model formation unit 256 ... classification model determination / formation unit 260 ... cell classification unit 270 ... search object temporary storage unit 272 ... cell object storage unit 274 ... extraction condition storage unit 276 ... candidate classification model Storage unit 280 ... Classification model storage unit 290 ... Cell catalog storage unit 311 ... Incubator 319 ... Culture vessel

Claims (10)

An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell A classification model generation device that generates a classification model for classification every time,
A cell object recognizing unit for recognizing an imaging region of each adhesive cell as an individual cell object from a known identical attribute captured image that is an image obtained by imaging a plurality of known adhesive cells having the same attribute;
A characteristic object extraction unit that extracts the cell object having a predetermined morphological characteristic amount as a characteristic object from the cell objects;
A candidate classification model forming unit that forms a candidate classification model that is a candidate for the classification model by using a morphological feature amount of the characteristic object and a plurality of attributes of the adhesion type cells recognized as the characteristic object;
A classification model determining unit that determines the classification model from among the plurality of candidate classification models formed by the candidate classification model forming unit according to the extracted characteristic object ;
The candidate classification model forming unit
As the morphological feature amount of the characteristic object, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, Using at least one morphological feature value of a value indicating a width when assumed to be linear, and as a plurality of attributes of the adherent cell recognized as the characteristic object, a name, culture A classification model generation device, characterized in that the candidate classification model is formed using a plurality of attributes including conditions and activities. An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell A classification model generation device that generates a classification model for classification every time,
A cell object recognizing unit for recognizing an imaging region of each adhesive cell as an individual cell object from a known identical attribute captured image that is an image obtained by imaging a plurality of known adhesive cells having the same attribute;
A classification model generation unit that generates the classification model based on the morphological feature of the cell object and a plurality of attributes of the adhesive cells recognized as the cell object;
The classification model generation unit includes:
As the morphological feature amount of the cell object, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, and the adherent cells are simulated. Using at least one of the morphological features of the value indicating the width when assumed to be linear, and as a plurality of attributes of the adherent cells recognized as the cell object, a name, a culture condition, A classification model generation apparatus, wherein the classification model is formed using a plurality of attributes including activity. An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell A classification model generation device that generates a classification model for classification every time,
A cell object recognizing unit for recognizing an imaging region of each adhesive cell as an individual cell object from a known identical attribute captured image that is an image obtained by imaging a plurality of known adhesive cells having the same attribute;
A classification model generation unit that generates the classification model based on the morphological feature of the cell object and a plurality of attributes of the adhesive cells recognized as the cell object;
The classification model generation apparatus characterized in that the plurality of attributes of the adherent cell recognized as the cell object are a plurality of attributes including a name, a culture condition, and an activity. An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell A classification model storage unit for storing a classification model to be classified every time;
A cell object recognition unit for recognizing an imaging region of each adhesive cell from the unknown attribute captured image as an individual cell object;
By inputting the morphological feature amount of the cell object recognized from the unknown attribute captured image by the cell object recognition unit or the feature amount calculated from the morphological feature amount into the classification model, the unknown attribute imaging A cell classification unit for classifying adhesive cells in the image or the unknown attribute captured image ;
A cell object storage unit that stores the cell object recognized from a known identical-attribute captured image, which is a captured image in which a plurality of adhesion-type cells with the same attribute known by the cell object recognition unit are captured;
When the cell classification unit classifies the unknown attribute picked-up image or the adherent cell in the unknown attribute picked-up image, a predetermined morphological feature amount is selected from the cell objects stored in the cell object storage unit. A candidate classification model that is extracted as a characteristic object and that is a candidate for the classification model using a morphological characteristic amount of the characteristic object and a plurality of attributes of the adhesion-related cells recognized as the characteristic object A classification model generation unit that determines the classification model from among the plurality of candidate classification models generated according to the extracted characteristic object, and
The classification model forming unit
As the morphological feature amount of the characteristic object, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, Using at least one morphological feature value of a value indicating a width when assumed to be linear, and as a plurality of attributes of the adherent cell recognized as the characteristic object, a name, culture A cell classification device, wherein the candidate classification model is formed using a plurality of attributes including conditions and activities. An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell A classification model storage unit for storing a classification model to be classified every time;
A cell object recognition unit for recognizing an imaging region of each adhesive cell from the unknown attribute captured image as an individual cell object;
By inputting the morphological feature amount of the cell object recognized from the unknown attribute captured image by the cell object recognition unit or the feature amount calculated from the morphological feature amount into the classification model, the unknown attribute imaging A cell classification unit for classifying adhesive cells in the image or the unknown attribute captured image;
A cell object storage unit that stores the cell object recognized from a known identical-attribute captured image, which is a captured image in which a plurality of adhesion-type cells with the same attribute known by the cell object recognition unit are captured;
When the cell classification unit classifies the unknown attribute captured image or the adhesion-related cells in the unknown attribute captured image, the morphological feature amount of the cell object stored in the cell object storage unit and the cell object A classification model generation unit configured to generate the classification model based on a plurality of attributes of the recognized adhesive cells ;
With
The classification model generation unit includes:
As the morphological feature amount of the cell object, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, and the adherent cells are simulated. Using at least one of the morphological features of the value indicating the width when assumed to be linear, and as a plurality of attributes of the adherent cells recognized as the cell object, a name, a culture condition, A cell classification device, wherein the classification model is formed using a plurality of attributes including activity. An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell A classification model storage unit for storing a classification model to be classified every time;
A cell object recognition unit for recognizing an imaging region of each adhesive cell from the unknown attribute captured image as an individual cell object;
By inputting the morphological feature amount of the cell object recognized from the unknown attribute captured image by the cell object recognition unit or the feature amount calculated from the morphological feature amount into the classification model, the unknown attribute imaging A cell classification unit for classifying adhesive cells in the image or the unknown attribute captured image;
A cell object storage unit that stores the cell object recognized from a known identical-attribute captured image, which is a captured image in which a plurality of adhesion-type cells with the same attribute known by the cell object recognition unit are captured;
When the cell classification unit classifies the unknown attribute captured image or the adhesion-related cells in the unknown attribute captured image, the morphological feature amount of the cell object stored in the cell object storage unit and the cell object A classification model generation unit configured to generate the classification model based on a plurality of attributes of the recognized adhesive cells ;
With
The cell classification apparatus characterized in that the plurality of attributes of the adherent cell recognized as the cell object are a plurality of attributes including a name, a culture condition, and an activity. Accommodates a culture vessel for culturing adhesive system cells, and an imaging device for imaging a thermostatic chamber capable of maintaining inside a predetermined environmental condition, the image of the adhesive system cells contained in the culture vessel in the thermostatic chamber, wherein in Louis Nkyubeta a cell sorting device according to claim 4 to claim 6,
An incubator further comprising a culture condition changing unit that changes a culture condition of the adherent cells in the culture container based on a result of the classification of the adherent cells using the classification model. A method for culturing cells, comprising using the incubator according to claim 7 . An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell In the computer of the classification model generation device that generates the classification model to classify every time,
Recognizing cell object recognition step the imaging area of the individual adhesive system cells as individual cells object from a known identity attributes captured image a plurality of adhesive systems cells are image captured Known same attributes,
A characteristic object extracting step of extracting the cell object having a predetermined morphological characteristic amount from the cell object as a characteristic object;
A candidate classification model forming step of forming a candidate classification model that is a candidate for the classification model using a morphological feature amount of the characteristic object and a plurality of attributes of the adhesion-related cells recognized as the characteristic object;
From the plurality of the candidate classification model formed in the candidate classification model formed step in accordance with the extracted said characteristic objects, a program for executing the <br/> a classification model determining step of determining the classification model There,
The candidate classification model forming step includes:
As the morphological feature amount of the characteristic object, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, Using at least one morphological feature value of a value indicating a width when assumed to be linear, and as a plurality of attributes of the adherent cell recognized as the characteristic object, a name, culture A program characterized in that the candidate classification model is formed using a plurality of attributes including conditions and activities. An unknown attribute imaging image that is an image obtained by imaging an unknown adhesive cell that adheres to a culture vessel, or an adhesion system cell in the unknown attribute imaging image, based on the morphological characteristics of the adhesion cell In the computer of the cell classification device that stores the classification model to be classified for each,
A cell object recognition step for recognizing an imaging region of each adhesive cell from the unknown attribute captured image as an individual cell object;
The unknown attribute imaging is performed by inputting a morphological feature amount of the cell object recognized from the unknown attribute captured image in the cell object recognition step or a feature amount calculated from the morphological feature amount into the classification model. A cell classification step for classifying adhesive cells in the image or the unknown attribute captured image ;
A cell object storing step of storing the cell object recognized from a known identical attribute captured image, which is a captured image in which a plurality of adhesion-type cells of the same attribute known by the cell object recognition step are captured;
A predetermined morphological feature amount among the cell objects stored in the cell object storing step when the unknown attribute picked-up image or the adherent cell in the unknown attribute picked-up image is classified by the cell classifying step. A candidate classification that is a candidate for the classification model using a morphological feature amount of the characteristic object and a plurality of attributes of adhesion-related cells recognized as the characteristic object. A classification model generation step of forming a model and determining the classification model from among the plurality of candidate classification models generated according to the extracted characteristic object ,
The classification model formation step includes:
As the morphological feature amount of the characteristic object, a value indicating the maximum value of the lines crossing the adherent cells, a value indicating the length when the adherent cells are assumed to be pseudo linear, Using at least one morphological feature value of a value indicating a width when assumed to be linear, and as a plurality of attributes of the adherent cell recognized as the characteristic object, a name, culture A program characterized in that the candidate classification model is formed using a plurality of attributes including conditions and activities.

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