JP2009122115A - Cell image analyzer - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a cell image analyzer that precisely extracts the total number of cells or the characteristic quantity of an individual cell. <P>SOLUTION: The cell image analyzer is equipped with: a cell image acquiring means 55 for imaging a plurality of cells developed in color by different dyes under different conditions to acquire a plurality of images; and an image analyzing means 80 for extracting predetermined element contained in the respective cells in a plurality of images. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a cell image analysis apparatus that extracts predetermined data by performing image processing on cell images of animals and plants.

  In the fields of biotechnology and drug development, work is being performed to confirm the effects and influences of substances such as various drugs and drugs on animal and plant cells and microorganisms, or the reactions of animal and plant cells and microorganisms to these substances. For example, when targeting cells with complex morphological features such as nerve cells, the position of each cell is recognized, and the total number of cells and the amount of morphological features of each cell are observed and recorded. , Work to convert to data is required.

  However, the observation work to count the total number of cells in complex forms depends on the researchers' visual observation, and the observation results fluctuate between researchers, and the observation results fluctuate due to fatigue due to long-term observation of the microscopic images. There was a problem to do. At present, a means for individually identifying a plurality of cells having complicated morphological features and automatically extracting their feature amounts has not been realized, or sufficient accuracy cannot be realized.

  In particular, in the case of drug screening, it is necessary to change the conditions of the cells to be the sample in various ways, so that the number of cells to be observed becomes enormous. For this reason, there is a strong need for an apparatus that automatically recognizes the acquired cell image.

  For this reason, a technique (see Patent Document 1) that detects an imaging position of a subject that is optimal for analysis and a technique that can correctly extract a shape from a cell image (see Patent Document 2) are disclosed.

  In screening using cells, there is a method that identifies individual cells from cell images, determines the boundaries of those cells, extracts the features of individual cell shapes, and quantifies and compares the effects of drugs. It is general (refer patent document 3).

  A particularly difficult and important process in image analysis of these series of cells is the individual identification of the cells. If the cells are mistakenly identified, the number of cells is inaccurate. In this case, the reliability of the characteristic amount data of each cell processed statistically is lost. For these reasons, image processing with good accuracy is required.

  In general, an image of a cell is a remnant of an organ of a dead cell, debris other than a cell that was entered in a process performed on the cell before imaging, as well as bubbles, and partial images that occur because the imaging device is out of focus. Including cells with unclear boundaries. These are one of the causes that are difficult.

  One advantage of an image analysis device equipped with an analysis processing program is that it directly automates observation by humans, but the various difficult elements included in the image as described in the previous section are particularly noticeable when the processing is automated. become.

  Although depending on the state at the time of imaging, the cells often appear to overlap each other due to the spatial arrangement, and the overlap of these cells makes another cell difficult to recognize and identify. It is cited as the cause.

  In this way, when cells overlap, the boundaries of the cells are intricately intertwined, so it is difficult to identify the area that each cell occupies on the image. The means for recognizing these cells is not realistic in terms of accuracy.

  There are too many mistakes to be recognized when recognizing individual cells.

  “Overlook” means that what is supposed to be recognized as a cell is not recognized as a cell, and “too many” results from recognizing something other than a cell as a cell.

  Among these, as an example of oversight, when two or more cells overlap in the image and it is difficult to recognize the boundary between these individual cells, these cells are identified as a single cell. , The situation can be considered.

  On the other hand, over-counting is one example in which the number of cells in a region where cells overlap is estimated beyond the original number because the boundary is recognized too much. .

  Furthermore, images often contain dust other than cells, but they are recognized as cells, so that they are overcounted.

JP2001-242085 JP2002312761 Patent publication 2000-509827

  An object of the present invention is to provide a cell image analysis apparatus capable of accurately extracting the total number of cells and the feature amount of each individual cell.

  The cell image analysis apparatus according to an aspect of the present invention acquires images having a plurality of different characteristics for the same sample by staining a sample containing cells with a plurality of different methods and causing the sample to emit light. By overlaying and combining images taken under these multiple different conditions, the accuracy of recognizing individual cells is improved, and individual cells are identified from cells with complex morphological features. And the feature quantity of each cell can be extracted with high accuracy.

  The cell image analysis apparatus according to an embodiment of the present invention includes a cell image acquisition unit that captures a plurality of cells that are colored by different staining under different conditions, and acquires a plurality of images. An image analyzing means for extracting a predetermined element contained in each cell is provided.

  A cell image analysis apparatus according to another embodiment of the present invention is characterized in that a cell is recognized using a plurality of images obtained by imaging a plurality of cells under different conditions.

  According to the present invention, it is possible to correctly recognize cells having individual complex morphological features, accurately count the total number of these cells, and automatically extract feature amounts of individual cells from cell images. it can.

1 is a block diagram showing a configuration of an image processing apparatus according to an embodiment of the present invention.

The flowchart which shows the whole flow of the image processing which concerns on one embodiment of this invention.

The flowchart which shows the flow of the center point determination (examination) which concerns on one embodiment of this invention.

The flowchart which shows the flow in the case of extracting a center point from the single image which concerns on one embodiment of this invention.

The figure for demonstrating the method of determining a cell center for the case where a cell takes an irregular shape as an example.

The figure for demonstrating the method of calculating | requiring a center point using the extracted cell boundary.

The figure for demonstrating the determination method of a specific cell center.

The figure for demonstrating the process when the cell nucleus has protruded out of the cell membrane.

The figure for demonstrating the determination method when two cell centers exist in a cell boundary by a cell center determination part.

The figure which shows the case where different types of cells overlap in a complicated manner.

The figure shown about the acquisition method of the three-dimensional image of a cell.

The figure for demonstrating the effect of a three-dimensional image process.

  Embodiments of the present invention will be described with reference to the drawings. A basic configuration for carrying out the present invention will be described with reference to FIG. FIG. 1 is a block diagram illustrating a configuration of an image processing apparatus according to an embodiment of the present invention, and illustrates a scanning confocal optical microscope as a basic configuration of a cell image analysis apparatus according to the present embodiment.

  For example, an argon laser having a wavelength of 488 nm is used as the light source 20. The laser beam emitted from the light source 20 is expanded in beam diameter by the collimator lens 22, converted into a parallel light beam, and guided to the dichroic mirror 25. The laser light guided to the dichroic mirror 25 is reflected by the dichroic mirror 25, enters the XY scanner 30, and is scanned by the XY scanner 30 in the XY axis direction. The laser light that has passed through the XY scanner 30 enters the objective lens 35 and is focused on a solution-like sample 42 that is a measurement target. The dichroic mirror 25 reflects light having a wavelength of laser light from the light source 20 and transmits light having a wavelength longer than the wavelength of laser light from the light source 20. Further, a galvanometer mirror is usually used as the XY scanner 30.

  The sample 42 is labeled with a fluorescent substance, and is placed on a microplate 41 placed on a sample stage 40 of a microscope, which will be described in detail later. For example, rhodamine green (RhG) is used as the fluorescent material. The fluorescence emitted from the sample 42 passes through the objective lens 35 again, passes through the XY scanner 30, passes through the dichroic mirror 25, is reflected by the mirror 50, and reaches the lens 51. The fluorescent light is collected by the lens 51, focused at the opening of the pinhole 52, and then received by the photodetector 55. The fluorescence signal received by the photodetector 55 is converted into a photocurrent signal and input to the image processing unit 80, and calculation such as signal processing and image analysis described later is performed in detail. A fluorescent image of the sample is extracted on the monitor. Note that a CCD (Charge Coupled Device) camera is normally used as the light detector 55, but a CMOS (Complementary Metal Oxide Semiconductor) image sensor or the like may be used.

As a container for accommodating the sample 42, for example, a 96-hole microplate 41 having a transparent bottom surface is used. The space between the tip of the objective lens 35 and the microplate 41 is filled with immersion water. The microplate 41 is held on the sample stage 40. Two stepping motors (not shown) are attached to the sample stage 40 so that the sample stage 40 can move in two directions orthogonal to each other, and each stepping motor is connected to the sample stage controller 45. . The sample stage controller 45 is connected to the computer 10, holds the sample stage 40, and moves the sample stage 40 in the X direction and the Y direction by driving a stepping motor according to a command from the computer 10, thereby Place the face in the correct position,
In addition, by moving the objective lens 35 in the Z direction, the focus position of the laser light can be moved up and down along the Z-axis direction.

  Note that the fluorescent material is not limited to rhodamine green, and can correspond to the wavelength of the light source. -Orange, Texas-Red, etc. may be used.

  The output from the photodetector 55 is input to the image processing unit 80 and various image processing is performed. The image processing unit 80 includes the following processing units. First, based on the output from the light detector 55 (in this specification, unless otherwise specified, “photographed images” are output), the cell center extraction processing unit 81 is configured to output individual images in the image. A point that is the center of the cell (hereinafter referred to as “cell center”) is detected. The cell center determination unit 82 compares the points extracted by the cell center extraction processing unit 81 between a plurality of images, and determines a point excluding the point determined to be inappropriate as the cell center as the cell center. The cell boundary extraction unit 83 extracts a boundary surrounding the cell center point determined by the cell center determination unit 82. The feature amount extraction processing unit 84 extracts morphological feature amounts of individual cells based on the result of the cell boundary extraction unit 83. The total cell number extraction processing unit 85 analyzes the result of the feature amount extraction processing unit 84 from the cell center extraction processing unit 81 and extracts the number of cells included in the image.

  In addition, as hardware which comprises the image process part 80, the general purpose processing apparatus which operate | moves by a computer program like a general purpose computer or a personal computer can be used, for example. The image processing unit 80 may be a part of the computer 10. In the present embodiment, a computer program for causing the processing apparatus as described above to function as the image processing unit 80, that is, the cell center extraction processing unit 81, the cell center determination unit 82, the cell boundary extraction unit 83, and the individual cell A program for realizing the functions of the feature amount extraction processing unit 84 and the total cell number extraction processing unit 85 is installed to constitute a cell image processing apparatus.

  The operation of the cell image analysis apparatus according to the embodiment of the present invention configured as described above will be described with reference to the flowcharts of FIGS. 2 is a flowchart showing the overall flow of image processing according to an embodiment of the present invention. FIG. 3 is a flowchart showing the flow of center point determination (examination) according to an embodiment of the present invention. FIG. 4 is a flowchart showing a flow when a center point is extracted from a single image according to an embodiment of the present invention.

  With reference to FIG. 2, the overall flow of the image processing according to the present embodiment will be described. First, as a preparation for photographing, a cell to be a sample 42 under various conditions to be observed is prepared, and an operation for performing appropriate staining and light emission is performed on the cell (hereinafter also referred to as “sample”). Against. These samples are placed on the sample stage 40 and their positions are fixed. When these preparations are completed, the image of the sample 42 is detected (captured) by the photodetector 55 (for example, a digital camera) (step A1). In this case, the sample is photographed while changing the imaging condition by changing the filter a required number of times (that is, a plurality of times) while the position of the sample 42 is fixed. Further, the sample 42 is imaged while changing the position of the sample using the microplate 41. Next, the center point of each cell (that is, the cell center) is extracted for each image (step A2). Then, the cell center obtained in step A2 is collated between a plurality of images to examine whether it is a cell center and to determine the cell center (step A3). Next, the boundary of each cell is extracted based on the cell center obtained in step A3 (step A4). And based on said analysis result, the feature-value of each cell is extracted or the result which counted the total number of cells is output (step A5).

  A case where cells are stained by a plurality of different staining methods in the case where a sample is imaged to extract a feature amount as described above will be described.

  In general, cell images are often imaged by magnifying the light developed by these operations using a microscope or the like by staining intracellular proteins and intracellular nuclei or using fluorescent proteins.

  In particular, in a sample in which a plurality of types of cells are mixed, the operation can be performed so that only specific types of cells emit light by the staining method. Using this property, a characteristic staining method or luminescence operation can be performed on cells to be observed.

  Then, by preparing a plurality of filters that transmit light in different frequency bands and imaging the same place of cells through different filters, the same object (cell) is imaged under different conditions. Images can be acquired.

  The flow of the cell center determination (examination) process is shown in FIG. In FIG. 3, the same parts as those in FIG. A plurality of images are picked up as described above, and images of the same cell and at the same position are picked up under a plurality of conditions (step A1). Next, candidate cell centers for each cell in each image are extracted (step A2). Then, for each point in the image extracted as a cell center candidate, the closest point in a different image is detected as a partner point to be paired (step B1). This partner point is similarly detected for each point of other images. Next, as will be described in detail later, it is determined whether or not the cell nucleus region is completely included in the cell membrane region (step B2). Then, on the condition that the partner points in the plurality of images match, the point where the partner points in the plurality of images match is determined as the cell center (step B3). Thereby, the correct cell center can be extracted.

  In the above processing, the center point (candidate) extraction processing in each image in step A2 will be described with reference to FIG. First, the boundary between a cell and another substance (for example, a solvent) is enhanced from the captured image by image processing (step C1). Next, a binarization process is performed on the image after the enhancement process to detect a boundary (step C2). When the boundary is detected, the feature amount at the coordinates (x, y) of each point in the boundary is extracted (step C3), and a threshold value for the extracted feature amount is set (step C4). Then, characteristic points based on the set threshold value are extracted (step C5), the neighboring points are collected (step C6), and the center point (candidate) is extracted (step C7).

  With reference to FIG. 5, a method for determining a cell center will be described by taking as an example a case where a cell has an irregular shape. In FIG. 5, (a) is a figure which shows the case where all the one cell bodies are dye | stained, (b) is a figure at the time of dyeing | staining only a cell nucleus. (C) is a diagram in which (a) and (b) are overlapped, and (d) is a cell center (denoted by ★) where the overlapping portion of (a) and (b) is the cell center. FIG.

  First, in order to obtain an image as shown in FIGS. 5A and 5B, the image is binarized with an appropriate value, and the boundary between 0 and 1 is set as the boundary of the cell body (or cell nucleus). And so on. Thereby, a cell body boundary as shown in (a) and a cell nucleus boundary as shown in (b) are extracted. Note that the cell nucleus image in FIG. 5 displays the outline of the binarized cell nucleus, but the cell nucleus is usually circular or elliptical. However, when a plurality of cells are close to each other or three-dimensionally overlap, a complicated shape as shown in the figure is shown.

  Next, the center point of each image surrounded by the cell body boundary and the cell nucleus boundary is extracted using a predetermined algorithm. In this case, when only the cell body image is used, it cannot be determined whether the extracted boundary is formed by a single cell or a plurality of cells. For example, the candidate for the center point of the cell is estimated as the cell body image and the center of the cell nucleus in FIGS. 5 (a) and 5 (b). Then, (a) and (b) can be superimposed as shown in (c) to determine the cell center as shown in (d).

  Here, there are several ways to find the center point. In the case of a cell nucleus image or the like, the amount of light emitted from the nucleus after staining tends to be highest at the center of the cell nucleus and less emitted around the nucleus compared to these. Therefore, the center of the cell nucleus can be obtained as a position where the luminance in the image is locally increased.

Conversely, a cell image captured by a method such as staining a cell membrane or causing a specific protein in a cell to emit light does not necessarily have the characteristics described above.
For these images, a method of obtaining the center point using the extracted cell boundary is considered suitable. Specifically, there are methods such as obtaining the center-of-gravity point of the cell boundary, or paying attention to the width of the boundary, obtaining the maximum width, and setting the middle point as the center point. For example, FIG. 6 shows an example in which two cells are partially overlapped. In this case, since two local maximum values are obtained in the image, both local maximums are lengths. If it is obtained as l, the position of the intermediate point l / 2 can be estimated or determined as the cell center.

  A specific cell center determination method will be described with reference to FIG. In FIG. 7 as well, an analysis using two images of a cell body image and a cell nucleus image will be described as an example. First, for each image, a point that is a candidate for a cell center is extracted. At this time, three points of points a1, a2, and a3 are extracted as cell center candidates in the cell body image, and three points of points b1, b2, and b3 are extracted as cell center candidates in the cell nucleus image. Shall. Next, the two images are overlapped, and the partner point that is the shortest distance of the extracted cell center candidates is detected with reference to the cell center candidates of other images. At this time, for example, the candidate point a1 is taken, and the point closest to this point is detected from the points b1, b2, and b3. If the closest point is point b2, the partner point of point a1 is point b2. In this way, partner points for the points a1, a2, and a3 are obtained, and similarly, partner points for the points b1, b2, and b3 are obtained. In this way, the closest point on the cell nucleus to each point on the cell body image is the partner point for each candidate, and each point on the cell nucleus image is closest to the center point candidate on the cell body image Points are partner points for each candidate. As a result, a partner point is obtained for each point of the two images, but only those whose partner points coincide with each other are determined as cell centers. This determines the correct cell center.

  In addition, according to said method, the probability of the misdetection which recognizes objects other than cells, such as garbage contained in a sample, as a cell erroneously can be reduced. For example, in the case of the analysis using the image of the cell nucleus and the cell body as described above, since the object other than the cell such as dust does not have the nucleus, the positions of the partner points do not coincide with each other. It can be judged that it is not.

  In addition, it is preferable to satisfy | fill another condition with respect to the cell center determined by said method. For example, since the cell nucleus is an intracellular organ, the cell nucleus must always exist in the cell membrane.

  However, as shown in FIG. 8, it is conceivable that the cell center is erroneously determined and the cell nucleus protrudes outside the cell membrane. Thus, in order to exclude erroneously determined cell centers, it is preferable to add to each cell center on the condition that the nucleus region is completely included in the region in the cell membrane (in the figure ☆ The marked part is the part determined as the cell nucleus).

  With reference to FIG. 9, the operation of the cell boundary extraction unit 83 that detects individual cell boundaries from the cell centers detected as described above will be described. FIG. 9 is a diagram for explaining a determination method when there are two cell centers in the cell boundary by the cell center determination unit 82.

  In the case of a cell having a complicated shape and having a shape that is remarkably different depending on each individual, it is conventionally determined whether the cell included in this image is one or two from the boundary of the cell body image as shown in FIG. Judgment is impossible or difficult.

  In the present embodiment, sample images are acquired under different conditions ((a) in FIG. 9) and superimposed ((b) in FIG. 9) to determine the cell center ((c) in FIG. 9). ). As shown in (c) of FIG. 9, when the center detected by the cell center determination unit 82 is 2, the number of cells is determined to be 2. Therefore, in this case, it can be estimated that there is a boundary separating the cells. Therefore, when the cell center is correctly obtained according to the present embodiment, for example, as shown in FIG. 9C, for example, a broken line portion is set at the boundary between two cells by using an appropriate algorithm. By doing so, the boundary of each cell can be estimated.

  The results shown in FIG. 9 have a significant effect on the results when extracting the morphological features of each cell such as the perimeter and protrusions. For example, consider the case where the perimeter of a cell, the presence or absence of an outer segment organ of the cell, and the like are extracted as feature amounts.

  When only one cell body image in FIG. 9 (a) is used to recognize one cell, the perimeter of the cell is calculated as one cell, so that the perimeter of two cells is obtained. . As described above, the perimeter of the cell in this case is about twice the actual perimeter, that is, the perimeter of two cells, and the average perimeter is accurately estimated on an image in which these overlapping states occur frequently. I can't do that.

  As described above, according to the present embodiment, it is possible to increase the accuracy of the feature parameter of the cell due to erroneous recognition such as the number of cells.

  FIG. 10 is a diagram illustrating a case where different types of cells overlap in a complicated manner. FIG. 10A shows an example in which cells overlap each other. When simple binarization is performed on this image, as shown in FIG. 10 (b), two cells are almost overlapped, so that the number of cells is two from this binarized image. It is difficult to recognize.

  In contrast to the case where the cells overlap in this way, when the cell types are different (for example, cell type 1 and cell type 2), only one specific cell type is imaged. It is possible to acquire only the cell image (see FIG. 10C). In addition, when there is a means for staining or emitting light by distinguishing two cell types, the overlapped image can be divided into images for individual cells by using the cell analysis device according to the present embodiment. it can. As a result, the complexity of the image due to the overlapping of cells can be reduced, the number of cells can be accurately counted, and the morphological features of individual cells can be extracted better. Also, two images may be acquired by changing the filter of the optical system depending on the type of cell.

  In particular, the cell classification can be performed with high accuracy by the cell boundary detected by the above method. The classification is performed using morphological features such as the perimeter detected as described above. If the feature amount detected with high accuracy is used, the accuracy of cell classification can be improved.

In the above embodiment, the case where the cell image is a two-dimensional image has been described as an example. However, as illustrated in FIG. 11, by acquiring a three-dimensional image and performing image analysis on the three-dimensional image, Furthermore, the accuracy can be increased. For example, a tomographic image (two-dimensional image) of a cell as shown in FIG. 11A is taken while changing the position of the Z axis on a plane perpendicular to the Z axis with the height direction as the Z axis. As a result, a plurality of two-dimensional images having different heights as shown in FIG. Based on these two-dimensional images, a three-dimensional image of a cell as shown in FIG. 11C can be obtained. Note that the image processing method for a plurality of two-dimensional images can be processed by the same method as that described in the above embodiment, thereby performing processing such as accurate cell center determination in each two-dimensional image. be able to.
In this case, for example, when there is a cell as shown in FIG. 12A, for example, the two-dimensional image of the arrow B is an image as shown in FIG. The two-dimensional image of the arrow C is an image as shown in FIG. Thus, even if the two-dimensional image alone cannot capture the cell nucleus as shown in FIG. 12C, for example, it cannot be counted as a cell, for example, in the height direction (horizontal direction) However, it does not matter if it is a matter of course. By taking a plurality of cell images and obtaining a three-dimensional image, more accurate image analysis can be performed.

  The following inventions can be extracted from the above embodiments. Each of the following inventions may be applied alone or in appropriate combination.

A cell image analysis apparatus according to an embodiment of the present invention includes a cell staining unit that causes a plurality of cells to develop color by different staining, and a cell that captures a plurality of images by imaging the plurality of cells under different conditions. Image acquisition means and image analysis means for extracting a predetermined element contained in each cell in the plurality of images are provided. Here, the element contained in the cell is preferably a cell boundary, a cell body center, a cell nucleus center, or a cell protrusion. In the above cell image analysis apparatus, the following embodiments are preferable.
(1) The image analyzing means collates the estimated cell centers with the means for estimating the cell centers of the plurality of cells for each of two or more different images. Means for determining the cell center.
(2) The image analysis means is configured to estimate each cell center of the plurality of cells by using a different method for each of two or more different images, and determine the estimated cell center between the plurality of images. Having means for determining cell centers by collating.
(3) Count the number of cells with the element.
(4) Estimating the shape, form, and boundaries of individual cells with the elements. Here, the shape, form, and boundary of each cell are determined using the cell center of each cell.
(5) Classifying cells based on the elements.
(6) The cell image acquisition means acquires at least an image of a cell nucleus.
(7) The cell image acquisition means acquires at least an image of a cell membrane.
(8) The means for estimating the cell center is to estimate the cell center using at least an image of a cell nucleus.
(9) The means for estimating the cell center is to estimate the cell center using at least an image of a cell membrane.
(10) The cell image acquisition means includes imaging means for capturing a plurality of tomographic images of cells in parallel and different planes, and a three-dimensional construction means for constructing a three-dimensional image from the plurality of tomographic images.
(11) The image analysis means performs image processing on the tomographic image of the planar cell.

  A cell image analysis device according to another embodiment of the present invention comprises a cell image acquisition means for coloring a cell membrane and at least one intracellular organ with different staining or fluorescent proteins, and acquiring these images, respectively. Region determining means for respectively determining a cell membrane and a region of the intracellular organ with respect to the image of the cell membrane obtained by the cell image acquisition means and the image of the intracellular organ; and the region of the intracellular organ is the cell membrane And determining means for determining as a cell when included in the region. In this cell image analysis apparatus, it is preferable that the intracellular organ is a cell nucleus, and further, the cell image acquisition unit includes an imaging unit that captures a plurality of tomographic images of cells in parallel and different planes, and the plurality of tomograms. Three-dimensional construction means for constructing a three-dimensional image from the image, wherein the region determination means determines the region of the cell membrane and the organ in the cell with respect to the three-dimensional image, and the determination means includes the three-dimensional image It is preferable to determine as a cell with respect to the dimensional image.

  A cell image analysis apparatus according to still another embodiment of the present invention is characterized in that a cell is recognized using a plurality of images obtained by imaging a plurality of cells under different conditions. Here, the different conditions are preferably a staining method and / or a coloring condition. In addition, the staining method preferably uses staining of a specific protein in a cell body, staining of a nucleus, or color development during gene expression using a fluorescent protein.

  The present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the scope of the invention at the stage of implementation. Further, the above embodiments include inventions at various stages, and various inventions can be extracted by appropriately combining a plurality of disclosed constituent elements.

  In addition, for example, even if some structural requirements are deleted from all the structural requirements shown in each embodiment, the problem described in the column of the problem to be solved by the invention can be solved, and the effect described in the effect of the invention Can be obtained as an invention.

DESCRIPTION OF SYMBOLS 10 ... Computer 20 ... Light source 22 ... Collimating lens 25 ... Dichroic mirror 30 ... XY scanner 35 ... Objective lens 40 ... Sample stage 41 ... Microplate 42 ... Sample 45 ... Sample stage controller 50 ... Mirror 51 ... Lens 52 ... Pinhole 55 ... Photodetector 80 ... Image processing unit 81 ... Cell center extraction processing unit 82 ... Cell center determination unit 83 ... Cell boundary extraction unit 84 ... Feature amount extraction processing unit 85 ... Total number extraction processing unit

Claims (12)

Cell image acquisition means for acquiring a plurality of images by imaging a plurality of cells colored by different staining under different conditions;
A cell image analysis apparatus comprising image analysis means for extracting a predetermined element contained in each cell in the plurality of images.   The cell image analysis apparatus according to claim 1, wherein the image analysis means includes means for estimating each cell center of the plurality of cells for each of two or more different images, and the estimated cell center. A cell image analysis apparatus comprising means for determining a cell center by collating the plurality of images.   2. The cell image analysis apparatus according to claim 1, wherein the image analysis unit is configured to estimate each cell center of the plurality of cells by a different method for each of two or more different images, and the estimation. A cell image analyzing apparatus comprising means for determining a cell center by comparing the cell center between the plurality of images.   A cell image analyzer characterized by recognizing a cell using a plurality of images obtained by imaging a plurality of cells under different conditions.   The cell image analysis apparatus according to claim 1, wherein the cell image acquisition unit acquires at least an image of a cell nucleus.   The cell image analysis apparatus according to claim 1, wherein the cell image acquisition unit acquires at least an image of a cell membrane.   4. The cell image analysis apparatus according to claim 2, wherein the means for estimating the cell center estimates the cell center using at least an image of a cell nucleus.   4. The cell image analyzing apparatus according to claim 2, wherein the means for estimating the cell center estimates the cell center using at least an image of a cell membrane. Cell image acquisition means for coloring the cell membrane and at least one intracellular organ with different staining or fluorescent protein, and acquiring each of these images;
Area determining means for determining the area of the cell membrane and the organ of the cell with respect to the image of the cell membrane obtained by the cell image acquisition means and the image of the organ of the cell;
And a determination means for determining as a cell when the area of the intracellular organ is included in the area of the cell membrane.   10. The cell image analysis apparatus according to claim 9, wherein the intracellular organ is a cell nucleus. In the cell image analysis device according to any one of claims 1 to 3,
The cell image acquisition means includes imaging means for imaging a plurality of tomographic images of cells in parallel and different planes, and a three-dimensional construction means for constructing a three-dimensional image from the plurality of tomographic images,
The cell image analysis apparatus, wherein the image analysis means performs image processing on a tomographic image of the planar cell. In the cell image analysis device according to claim 9 or 10,
The cell image acquisition means includes imaging means for imaging a plurality of tomographic images of cells in parallel and different planes, and a three-dimensional construction means for constructing a three-dimensional image from the plurality of tomographic images,
The region determining means determines the region of the cell membrane and the organ in the cell with respect to the three-dimensional image,
The cell image analysis apparatus characterized in that the determination means determines the cell as a cell with respect to the three-dimensional image.

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