JP2013085546A - Apparatus, method and program for processing image - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable to quantitatively observe the morphological change of a sample.SOLUTION: In an apparatus for processing images, a detection part 52 detects a region of osteoclast precursor cells as a sample 18 from the observed image of the sample 18 whose form changes time-dependently. An superposing part 53 superposes the osteoclast precursor cell regions in the observed images at different times, an operation part 54 calculates the degree of morphological change of the osteoclast precursor cells depending on the area of the superposed region of the osteoclast cells to decide if the osteoclast cells have been differentiated or not depending on the degree of the morphological change. A processing part 55 displays the osteoclast precursor cells (matured osteoclast cells) and undifferentiated osteoclast cells in the observed images with different display forms. The invention can be applied to an observation system using a confocal microscope.

Description

  The present invention relates to an image processing apparatus and method, and a program that allow a morphological change of a sample to be quantitatively observed.

  Conventionally, a sample whose form changes with the passage of time may be observed with a confocal microscope or the like (see, for example, Patent Document 1). For example, osteoclast precursor cells are known as samples whose morphology changes with time. Mature osteoclasts are cells that resorb bone, differentiate from a state called osteoclast precursor cells, and change to mature osteoclasts.

  If an image of the sample at each time is taken when observing such a sample, the user can check the process of changing the form of the sample by viewing the obtained image.

JP 2004-251630 A

  However, with the technique described above, it is possible to confirm the morphological change process of the sample to be observed, but it is not possible to quantitatively observe the morphological change of the sample.

  The present invention has been made in view of such a situation, and makes it possible to quantitatively observe a change in the form of a sample.

  The processing executed by the program of the present invention includes a detection step of detecting an observation target region from an observation image of the observation target whose form changes with time, and the observation target on the observation image at a predetermined time. A region common to both the region and the region to be observed on another observation image at a time before the predetermined time, and the region identified by the processing of the common region identification step Using at least one of the area of the common area of the observation object, the area of the observation object area on the observation image at the predetermined time, and the area of the observation object area on the other observation image, And a calculation step for calculating the degree of change in the shape of the observation target.

  According to the present invention, the morphological change of the sample can be observed quantitatively.

It is a figure which shows the structural example of one Embodiment of the observation system to which this invention is applied. It is a flowchart explaining an image processing process. It is a figure explaining calculation of the degree of form change. It is a figure which shows an example of the observation image after a process. It is a figure which shows an example of the data of the number of differentiated osteoclast precursor cells (mature osteoclast) and the number of undifferentiated osteoclast precursor cells at each time. It is a figure which shows an example of the data which show the value of the form change degree of each time. It is a figure which shows the other example of the data which show the value of the degree of a form change at each time.

  Embodiments to which the present invention is applied will be described below with reference to the drawings.

<First Embodiment>
[Configuration example of observation system]
FIG. 1 is a diagram showing a configuration example of an embodiment of an observation system to which the present invention is applied.

  This observation system includes a light source 11, a beam expander 12, a scan head 13, a microscope 14, a control unit 15, a computer 16, and a display unit 17, and observes a sample 18 to be observed. The sample 18 may be any sample as long as its shape (shape) changes with the passage of time, but in the following, the sample 18 is particularly osteoclast precursor cells (and osteoclast precursor cells). The description continues as if it were differentiated from mature osteoclasts.

  In the observation system, a scan head 13 that scans light is attached to a microscope 14, and the scan head 13 and the microscope 14 function as a confocal microscope with a two-photon microscope (hereinafter simply referred to as a confocal microscope). That is, with this confocal microscope, confocal observation by normal one-photon excitation and observation by the two-photon excitation method are realized.

  The light source 11 emits illumination light (excitation light) that irradiates the sample 18 to be observed. For example, when the sample 18 is observed by the two-photon excitation method, a short pulse laser light source or the like is used as the light source 11.

  The illumination light emitted from the light source 11 is shaped by the beam expander 12 so that the beam diameter is enlarged to be a parallel light beam, and enters the scan head 13.

  In this way, the illumination light guided to the scan head 13 by spatial propagation enters the microscope 14 via the dichroic mirror 31, the scan unit 32, and the relay lens 33 provided on the scan head 13. The illumination light incident on the microscope 14 is applied to the sample 18 placed on the stage 36 via the dichroic mirror 34 and the objective lens 35 in the microscope 14.

  At this time, the scanning unit 32 scans the illumination light on the sample 18 by deflecting the illumination light incident from the dichroic mirror 31 in the horizontal direction and the depth direction in the drawing. When the sample 18 is irradiated with illumination light, fluorescence that becomes observation light is generated from the sample 18, and this fluorescence enters the dichroic mirror 34 through the objective lens 35.

  In the case of the two-photon excitation method, fluorescence is generated only from the very vicinity of the condensing point and has high spatial resolution by itself, so that a pinhole is unnecessary. Therefore, in order to detect fluorescence with higher efficiency, a configuration is possible in which the detector is placed as close as possible to the sample.

  In the case of such a configuration, the dichroic mirror 34 reflects the fluorescence from the objective lens 35 as necessary and makes it incident on the condenser lens 37. That is, the dichroic mirror 34 is physically moved, and when observing the fluorescence generated from the sample 18 by the two-photon excitation method as it is, it is arranged on the optical path of the illumination light, that is, on the test optical path, When observing fluorescence by the normal one-photon excitation method by the confocal method, it is not arranged on the test light path.

  When the dichroic mirror 34 is disposed on the test light path, the dichroic mirror 34 transmits the illumination light from the relay lens 33 and reflects the fluorescence from the sample 18 to enter the condensing lens 37.

  The fluorescence incident on the condenser lens 37 is condensed by the condenser lens 37, received by the photoelectric detection element 38, and photoelectrically converted. The electrical signal obtained by the photoelectric conversion is supplied from the photoelectric detection element 38 to the computer 16 via the control unit 15.

  On the other hand, when the dichroic mirror 34 is not disposed on the test light path, the illumination light from the relay lens 33 is applied to the sample 18 through the objective lens 35. Then, the fluorescence from the sample 18 enters the dichroic mirror 31 through the optical path of the illumination light in the reverse direction. That is, the fluorescence that has entered the objective lens 35 from the sample 18 enters the dichroic mirror 31 via the relay lens 33 and the scanning unit 32.

  The dichroic mirror 31 reflects the fluorescence from the scanning unit 32 so as to enter the condenser lens 39. The dichroic mirror 31 transmits light having the wavelength of illumination light and reflects light having the wavelength of fluorescence.

  The fluorescence that has entered the condenser lens 39 from the dichroic mirror 31 is condensed by the condenser lens 39 and is received by the photoelectric detection element 41 through a pinhole provided in the confocal stop 40. The electrical signal obtained by photoelectrically converting the fluorescence received by the photoelectric detection element 41 is supplied from the photoelectric detection element 41 to the computer 16 via the control unit 15.

  Thus, in the case of confocal observation by the one-photon excitation method, fluorescence takes a path from the objective lens 35 to the photoelectric detection element 41. In the case of observation by the two-photon excitation method, detection by this path is possible by opening the pinhole.

  The computer 16 generates an image signal of the observation image of the sample 18 based on the electrical signal supplied from the photoelectric detection element 38 or the photoelectric detection element 41 via the control unit 15, and appropriately processes the observation image, The processing result is displayed on the display unit 17.

  That is, the generation unit 51 of the computer 16 generates an observation image based on the electrical signal supplied from the control unit 15 and supplies the observation image to the detection unit 52 and the processing unit 55. The detection unit 52 detects osteoclast precursor cells as the sample 18 from the observation image supplied from the generation unit 51, and supplies the detection result to the superposition unit 53.

  In addition, the superimposing unit 53 superimposes osteoclast precursor cells on the observation images captured at different times based on the detection result from the detection unit 52, and the calculation unit 54 observes the observation image superimposed by the superimposing unit 53. The degree of morphological change of osteoclast precursor cells is calculated based on the area of each region of the above osteoclast precursor cells. The processing unit 55 processes the observation image from the generation unit 51 on the display unit 17 based on the calculation result by the calculation unit 54.

[Description of operation of observation system]
Next, the operation of the observation system in FIG. 1 will be described.

  For example, when observation of the sample 18 by the two-photon excitation method is instructed, the light source 11 emits illumination light, and this illumination light passes through the beam expander 12 and the dichroic mirror 31 through the objective lens 35. 18 is irradiated. At this time, the scanning unit 32 scans the illumination light on the sample 18 by deflecting the illumination light.

  Then, fluorescence is emitted from the sample 18, and this fluorescence enters the dichroic mirror 34 through the objective lens 35, is further reflected by the dichroic mirror 34, is condensed by the condenser lens 37, and is detected by the photoelectric detection element 38. Is received. The photoelectric detection element 38 photoelectrically converts the received fluorescence and supplies the electrical signal obtained as a result to the generation unit 51 via the control unit 15.

  The generation unit 51 generates an observation image of the sample 18 based on the electrical signal supplied from the photoelectric detection element 38. Thereby, in the production | generation part 51, the image group which consists of the observation image of each continuous time is obtained. The generation unit 51 supplies the generated observation image to the detection unit 52 and the processing unit 55.

  Note that the observation image group generated by the generation unit 51 may be an image group including a plurality of still images or a moving image. In the following, among the observed images at each time, the nth observed image is also referred to as an observed image of frame n.

  Further, in the case of confocal observation by the one-photon excitation method, the dichroic mirror 34 is not arranged on the test optical path, and the fluorescence from the sample 18 is transmitted from the objective lens 35, the relay lens 33 to the dichroic mirror 31, and the condenser lens. 39 and the confocal stop 40 receive light by the photoelectric detection element 41. Then, the photoelectric detection element 41 performs photoelectric conversion on the received fluorescence, and the generation unit 51 generates an observation image at each time based on the electric signal obtained as a result.

  When the observation image at each time is generated in this way, the computer 16 performs image processing, processes the observation image, and causes the display unit 17 to display the processing result. Hereinafter, the image processing by the computer 16 will be described with reference to the flowchart of FIG.

  In step S <b> 11, the detection unit 52 detects osteoclast precursor cells that are observation targets from the observation image supplied from the generation unit 51.

  For example, the detection unit 52 determines, for each pixel of the observation image, whether or not the luminance value of the pixel is equal to or higher than a predetermined threshold value, and breaks an area including pixels whose luminance value on the observation image is equal to or higher than the threshold value. It is assumed that this is a region of osteoprogenitor cells. Alternatively, osteoclast precursor cells may be detected from the observed image by pattern matching, for example.

  In step S <b> 12, the detection unit 52 generates a mask image indicating the area of the osteoclast precursor cell on the observation image based on the detection result of the osteoclast precursor cell, and supplies the mask image to the superimposing unit 53. That is, for each pixel in the observation image, the detection unit 52 sets the pixel value of the pixel constituting the osteoclast precursor cell region to 1 and sets the pixel value of the pixel not constituting the osteoclast precursor cell region to 0. A mask image is generated. Therefore, on the mask image, an area composed of pixels having a pixel value of 1 is an osteoclast precursor cell area, and an area composed of pixels having a pixel value of 0 is an area without osteoclast precursor cells.

  Moreover, the detection part 52 provides ID which identifies those osteoclast precursor cells with respect to each osteoclast precursor cell on a mask image. At this time, the same ID is assigned to the same osteoclast precursor cells on different mask images.

  In step S <b> 13, the superimposing unit 53 superimposes the same osteoclast precursor cells on the mask image supplied from the detection unit 52, and supplies the result to the calculation unit 54. Here, the osteoclast precursor cells are identified by the ID assigned to each osteoclast precursor cell.

  For example, the superimposing unit 53 selects several frames of mask images as mask images of the processing target frame from among a plurality of consecutive frames of mask images. Specifically, frames arranged at predetermined intervals, such as 10 frames, are selected as processing target frames.

  When the processing target frame is selected, the superimposing unit 53 obtains the position of the center of gravity of the osteoclast precursor cell region for the osteoclast precursor cell on the mask image of each processing target frame. Then, the superimposing unit 53 superimposes the osteoclast precursor cells on the mask image so that the centroids of the same osteoclast precursor cells on the mask image overlap with each other in two processing target frames adjacent to each other in the time direction.

  As a result, for example, as shown in FIG. 3, the region R (t−1) of the osteoclast precursor cell in the (t−1) th processing target frame (t−1) and the t th processing target frame t are broken. The bone precursor cell region R (t) is superimposed.

  In the example of FIG. 3, these regions are overlapped so that the center of gravity of the region R (t−1) and the center of gravity of the region R (t) are at the position indicated by the arrow G. In this case, the larger the region RC where the region R (t-1) and the region R (t) overlap the region RH composed of the region R (t-1) and the region R (t), It can be said that the change in the morphology of osteoprogenitor cells is small.

  That is, when the region R (t-1) and the region R (t) of osteoclast precursor cells at different times are overlapped, the smaller the region RC common to these regions, the smaller the processing target frame (t-1). From this, it can be said that the osteoclast precursor cells have undergone a greater morphological change during the processing target frame t.

  The method for overlaying osteoclast precursor cells is not limited to the method for overlaying the center of gravity, and any method may be used.

  For example, the region R (t−1) is such that the center of the circle circumscribing the region R (t−1) of the osteoclast precursor cell and the center of the circle circumscribing the region R (t) of the osteoclast precursor cell overlap. ) And region R (t) may be overlapped. In addition, these regions may be overlapped so that the centers of the rectangles circumscribing the osteoclast precursor cell region overlap, or the polygonal centroids circumscribing the osteoclast precursor cell region overlap each other. Superposition may be performed.

  Returning to the description of the flowchart of FIG. 2, when the regions of the osteoclast precursor cells of the processing target frames adjacent to each other are overlapped, the process proceeds from step S13 to step S14.

  In step S <b> 14, the calculation unit 54 forms the form of each osteoclast precursor cell to be observed for each processing target frame based on the result of overlaying osteoclast precursor cells on the mask image supplied from the superimposing unit 53. The degree of change F is calculated.

  For example, when calculating the degree F of morphological change for osteoclast precursor cells in the region R (t) on the mask image of the processing target frame t illustrated in FIG. 3, the calculation unit 54 determines the area of the region RC as the region A value obtained by dividing by the area of RH is a value of the degree F of the shape change.

  In this case, the degree F of the shape change is a value in the range of 0 <F ≦ 1. Here, since the ratio of the common area | region RC with respect to the whole area | region RH becomes small, the morphological change degree F becomes small, so that the morphological change of the osteoclast precursor cell is large. On the contrary, if the morphological change of osteoclast precursor cells is small, the common region RC becomes large, and the degree F of morphological change becomes large.

  Thus, by dividing the area of the common region RC of the osteoclast precursor cells in the processing target frame that is temporally mixed by the area of the entire region RH of the superimposed osteoclast precursor cells, the osteoclast precursor cells It is possible to know the ratio of the area where the shape has changed with respect to the whole. Using the morphological change degree F thus obtained, the morphological change of osteoclast precursor cells can be quantitatively observed.

  In step S <b> 15, the calculation unit 54 performs threshold processing for the degree F of morphological change of each osteoclast precursor cell for each processing target frame, and supplies the result of the threshold processing to the processing unit 55. Specifically, when the value F of the morphological change degree of osteoclast precursor cells in a predetermined processing target frame t is equal to or greater than a predetermined threshold th, the calculation unit 54 determines whether the observation target frame t It is determined that osteoclast precursor cells have differentiated.

  For example, when osteoclast progenitor cells differentiate, osteoclast progenitor cells change in shape (shape) over time during the differentiation process, but when differentiation is completed and osteoclast progenitor cells become mature osteoclasts, Osteoclasts hardly change in shape.

  Therefore, the calculation unit 54 does not differentiate osteoclast precursor cells (not yet differentiated) during a period in which the degree F of morphological change of the osteoclast precursor cells of interest is less than the predetermined threshold th. Suppose that it is a period of differentiation. In addition, the calculation unit 54 assumes that the period during which the degree F of morphological change of osteoclast precursor cells is equal to or greater than the threshold th is a period during which osteoclast precursor cells have differentiated (a period in which differentiation has ended).

  Thus, the calculation unit 54 determines, for each osteoclast precursor cell, whether or not the osteoclast precursor cell has differentiated at each time based on the degree F of the morphological change obtained for the osteoclast precursor cell. Then, the time when osteoclast precursor cells differentiated is specified.

  Of the plurality of processing target frames arranged in chronological order, the last processing target frame in which the degree F of morphological change is less than the threshold th is detected, and the period before the detected processing target frame is an osteoclast precursor cell. May be in a state of not differentiated, that is, an undifferentiated state.

  Moreover, in the above, the case where it was determined whether or not osteoclast precursor cells were differentiated by threshold processing was described. However, the degree F of morphological change was compared with each of a plurality of threshold values, and osteoclast precursor cells were compared. It may be determined in which stage of the differentiation process is in the state. In other words, the differentiation process may be divided into a plurality of stages, and it may be determined which stage the osteoclast precursor cells are in.

  In step S <b> 16, the processing unit 55 processes the observation image supplied from the generation unit 51 based on the result of the threshold processing from the calculation unit 54, and supplies the observation image obtained as a result to the display unit 17. Display. At this time, the process part 55 acquires a mask image from the superimposition part 53 via the calculating part 54 as needed, and specifies the area | region of each osteoclast precursor cell with a mask image.

  For example, the processing unit 55 identifies whether the osteoclast precursor cells on the observation image of each frame are in a pre-differentiation state (undifferentiated state) or a differentiated state based on the threshold processing result. To do. Then, the processing unit 55 displays the observation image of each frame so that each osteoclast precursor cell on the observation image is displayed in a different display format depending on whether it is in an undifferentiated state or a differentiated state. Image processing is performed on the image.

  Thereby, for example, an observation image shown in FIG. 4 is obtained. In FIG. 4, regions Q11 to Q13 on the observation image are regions of osteoclast precursor cells as the sample 18.

  Here, the osteoclast precursor cells displayed in the region Q11 are differentiated, that is, the state of mature osteoclasts, and the osteoclast precursor cells displayed in the regions Q12 and Q13 are undifferentiated, that is, osteoclasts. It is assumed that the state is a progenitor cell. In such a case, a red frame is displayed in the region Q11, and a blue frame is displayed in the region Q12 and the region Q13.

  Thus, by displaying the differentiated osteoclast precursor cells (mature osteoclasts) and the undifferentiated osteoclast precursor cells in different display formats, the user can display the osteoclast precursor cells in each state on the observation image. It is possible to quickly grasp the region and to quantitatively observe the morphological change of osteoclast precursor cells.

  In addition, although the case where the frame of a different color was displayed on the area | region of the osteoclast precursor cell was demonstrated, the area | region Q11, the area | region Q12, and the area | region Q13 may be displayed with a different color, or the area | region Q11 and the area | region Q12 A blinking frame may be displayed in any of the areas Q13 and Q13.

  Returning to the description of the flowchart of FIG. 2, when the processed observation image is supplied to the display unit 17 and displayed, the image processing process ends.

  As described above, the computer 16 detects the osteoclast precursor cell region from the observation image, determines the degree of morphological change of the osteoclast precursor cell at each time, and observes using the obtained morphological change degree. Process the image. Thus, the morphological change of osteoclast precursor cells can be quantitatively observed by obtaining the degree of morphological change of osteoclast precursor cells at each time. Furthermore, if an observation image processed based on the degree of morphological change is displayed, it can be easily recognized whether or not osteoclast precursor cells have differentiated.

  In this embodiment, in order to specify the common region RC, for example, as shown in FIG. 3, the region R (t) of the osteoclast precursor cell of the (t−1) -th processing target frame (t−1). -1) and the osteoclast precursor cell region R (t) of the t-th processing target frame t are superimposed in the superimposing unit 53 in the computer 16, but the present invention is not limited to this. A common pixel address is extracted by extracting a common pixel address from the address of the pixel constituting the osteoprogenitor cell region R (t-1) and the address of the pixel constituting the osteoclast precursor cell region R (t). May be.

<Modification of the degree of form change>
In the above description, it has been described that the ratio F of the morphological change is obtained as the ratio of the region RC of the common part and the entire region RH where the osteoclast precursor cells are superimposed. Any value may be used as long as it indicates the relationship between the area of the entire progenitor cell and the common part.

  For example, values obtained by subtracting the area of the common region RC from the area of the entire region RH of the osteoclast precursor cells in FIG. 3 (the osteoclast precursor cell region R (t−1) and the region R (t) The value obtained by dividing the area of the non-common part region by the area of the osteoclast precursor cell region R (t) may be the morphological change degree F.

  Further, a value obtained by subtracting the area of the common region RC from the area of the entire region RH of osteoclast progenitor cells (the value common to the region R (t-1) and the region R (t) of osteoclast progenitor cells). A value obtained by dividing the area of the non-part area by the area of the common part area RC may be the degree F of the shape change.

  Further, the value obtained by multiplying the area of the common part region RC by 2 is obtained by dividing the value obtained by multiplying the area of the osteoclast precursor cell region R (t-1) and the area of the region R (t) by the sum. The value may be the degree F of the form change.

  Further, for example, a value obtained by subtracting the area of the region RC of the common portion from the area of the entire region RH of osteoclast precursor cells may be the morphological change degree F.

  Further, a value obtained by subtracting the area of the common region RC from the area of the osteoclast precursor cell region R (t) in the processing target frame t is obtained by dividing the value by the area of the region R (t). The obtained value may be the degree F of the shape change.

<Modification of output to display>
Moreover, in the above, as an example of quantitatively observing the morphological change of osteoclast precursor cells, the differentiation time of osteoclast precursor cells is specified, and in the observed image, differentiated osteoclast precursor cells (mature osteoclasts) and Although the case where undifferentiated osteoclast precursor cells are displayed in a different display format has been described, the number of osteoclast precursor cells differentiated may be displayed.

  For example, as another example of quantitatively observing the morphological change of the osteoclast precursor cells to be observed, as shown in FIG. 5, the number of differentiated osteoclast precursor cells (mature osteoclasts) in the observed images at each time And data indicating the number of undifferentiated osteoclast precursor cells may be generated. In the figure, the horizontal axis indicates time, and the vertical axis indicates the number of osteoclast precursor cells in the observed image.

  In the example of FIG. 5, the broken line C11 indicates the number of differentiated osteoclast precursor cells (mature osteoclasts) on the observation image, and the broken line C12 indicates the number of undifferentiated osteoclast precursor cells on the observation image. Show. As indicated by these lines, the number of differentiated osteoclast precursor cells (mature osteoclasts) increases with time, and conversely, the number of undifferentiated osteoclast precursor cells decreases with time. Yes.

  When a graph indicating the number of differentiated osteoclast precursor cells (mature osteoclasts) and the number of undifferentiated osteoclast precursor cells at each time is generated, the processing unit 55 performs threshold processing from the calculation unit 54. Based on the results, the number of differentiated osteoclast precursor cells (mature osteoclasts) and undifferentiated osteoclast precursor cells are determined on the observed image. And the process part 55 produces | generates the graph shown in FIG. 5 based on the number of the differentiated osteoclast precursor cell (mature osteoclast) and the undifferentiated osteoclast precursor cell in the observation image of each time, and a display part 17 is supplied.

  Thereby, the graph shown in FIG. 5 is displayed on the display unit 17. Even when such a graph is displayed, the user can quantitatively observe the morphological change of osteoclast precursor cells. Note that the data indicating the number of differentiated cells and the number of undifferentiated cells at each time is not limited to the graph shown in FIG. 5, and may be a table showing the number of differentiated cells and the number of undifferentiated cells at each time.

  As another example of quantitatively observing the morphological change of osteoclast precursor cells, for example, as shown in FIG. 6, data indicating the value F of the morphological change degree at each time of osteoclast precursor cells is generated. Also good. In the figure, the horizontal axis indicates the time, and the vertical axis indicates the degree F of the shape change.

  In the example of FIG. 6, the broken line C <b> 21 indicates the value of the degree F of morphological change at each time of one osteoclast precursor cell on the observation image. As indicated by the broken line C21, the degree F of morphological change of the osteoclast precursor cells rapidly increases after time “9”, and hardly changes after time “15”. Therefore, it can be seen that the osteoclast precursor cells are in a differentiated state around time “15” and are mature osteoclasts.

  When a graph showing the degree F of morphological change of osteoclast progenitor cells at each time is generated, the processing unit 55 applies the morphological change of each osteoclast progenitor cell on the observation image from the calculation unit 54 to the observation image at each time. F is obtained. And the process part 55 produces | generates the graph shown in FIG. 6 based on the value of the morphological change degree F in each time of the osteoclast precursor cell for every osteoclast precursor cell, and supplies it to the display part 17. FIG. That is, the graph shown in FIG. 6 is generated for each osteoclast precursor cell.

  Thereby, the graph shown in FIG. 6 is displayed on the display unit 17. Even when such a graph is displayed, the user can quantitatively observe the morphological change of osteoclast precursor cells. In particular, in this case, the user can visually grasp the speed at which osteoclast precursor cells differentiate (state of differentiation).

  Further, for example, the area of the non-common part of the osteoclast precursor cell region R (t-1) and the region R (t) in FIG. 3 is divided by the area of the osteoclast precursor cell region R (t). 7 is generated by the processing unit 55 and displayed on the display unit 17 when the obtained value is the degree F of the form change. In the figure, the horizontal axis indicates the time, and the vertical axis indicates the degree F of the shape change.

  In the example of FIG. 7, the polygonal line C31 indicates the value of the degree F of morphological change at each time of one osteoclast precursor cell on the observation image. As indicated by the broken line C31, the degree F of the morphological change of the osteoclast precursor cells rapidly decreases until the time “10”, and hardly changes after the time “10”.

  In this example, the degree F of the shape change is obtained by the ratio of the area of the non-common part of the region R (t−1) and the region R (t) and the area of the region R (t) as described above. . Further, if the morphological change of osteoclast precursor cells is small, the common region RC becomes large, so that the area of the region not common to the region R (t-1) and the region R (t) becomes small. Therefore, the smaller the morphological change of osteoclast precursor cells, the smaller the value F of the morphological change.

  According to the broken line C31, the degree of morphological change F is small at time “10” and the degree of morphological change F hardly changes after time “10”. In the vicinity, it is in a differentiated state, and it can be seen that it is a mature osteoclast.

  Note that the data indicating the value F of the form change at each time is not limited to the graphs illustrated in FIGS. 6 and 7 and may be a table indicating the value F of the form change at each time.

  Moreover, although this embodiment demonstrated the example which determines whether the osteoclast precursor cell differentiated based on the morphological change degree F calculated | required about the osteoclast precursor cell, it is not restricted to this, The form of a cell Based on the degree of change F, it may be determined whether or not the cell is activated.

  Furthermore, at the time of observing the sample 18, different drugs are added to the plurality of samples 18, and samples with different observation conditions are prepared for each sample 18. Then, the sample 18 is observed with fluorescence, and the drug effect is determined.

  In many samples 18, it is determined that appropriate differentiation from osteoclast precursor cells to mature osteoclasts has been performed from a graph showing the degree F of osteoclast precursor cell morphology change at each time. However, there is a case where it is determined in a sample 18 that appropriate differentiation from an osteoclast precursor cell to a mature osteoclast is not performed from a graph indicating the degree F of morphological change of osteoclast precursor cells at each time. It can be seen that the observation condition in the sample 18 has a drug effect. In this case, for example, the processing unit 55 determines that there is a drug effect under the observation condition.

  In addition, when the referrer of the graph indicating the degree F of morphological change of the osteoclast precursor cells at each time is set by the user's designation and the difference from the referrer is not less than a predetermined value, it is determined that there is a drug effect. You may make it do. In this case, the presence or absence of the drug effect for each sample can be determined quantitatively.

  When osteoclast precursor cells are cultured, the culture conditions such as the temperature and culture medium of the osteoclast precursor cells are determined based on the degree F of the morphological change obtained for the osteoclast precursor cells described in this embodiment. Also good. When determining the osteoclast precursor cell culture conditions using the observation system shown in FIG. 1, the temperature and medium are determined based on the degree F of osteoclast precursor cell morphology change calculated by the calculation unit 54 in the computer 16. It implement | achieves by providing the determination part which determines the culture conditions of osteoclast precursor cells, such as.

  In the present embodiment, the osteoclast precursor cell has been described as an example. However, the present invention is not limited to this and can be applied to other cells.

  The series of processes described above can be executed by hardware or can be executed by software. When a series of processing is executed by software, a program constituting the software is read from a recording medium by, for example, the computer 16 and recorded. Then, the program recorded in the computer 16 is executed by the computer 16 and the image processing shown in FIG. 2 is performed.

  The embodiments of the present invention are not limited to the above-described embodiments, and various modifications can be made without departing from the scope of the present invention.

  13 Scan Head, 14 Microscope, 16 Computer, 18 Sample, 51 Generation Unit, 52 Detection Unit, 53 Superimposition Unit, 54 Calculation Unit, 55 Processing Unit

Claims (36)

From the observation image of the observation object whose form changes with the passage of time, a detection step for detecting the region of the observation object;
A common area specifying step of specifying an area common to the observation target area on the observation image at a predetermined time and the observation target area on another observation image at a time before the predetermined time;
The area of the common area of the observation target specified by the processing of the common area specifying step, the area of the observation target area on the observation image at the predetermined time, and the observation target on the other observation image And a calculation step of calculating a degree of change in the shape of the observation target using at least one of the areas of the region. In the calculation step, the area of the common area of the observation target is determined by subtracting the common area of the observation target from the observation target area on the observation image at the predetermined time and the other observation image. The program according to claim 1, wherein the degree of the shape change is calculated by dividing by an area of a region including the region to be observed. In the calculation step, the area of the region that is not common to the region to be observed on the observation image at the predetermined time and the region to be observed on the other observation image is defined as the observation image at the predetermined time. The program according to claim 1, wherein the degree of form change is calculated by dividing by the area of the region to be observed above. In the calculation step, the area of the observation target area on the observation image at the predetermined time and the area of the observation target area on the other observation image that is not common are the areas of the observation target common area. The program according to claim 1, wherein the degree of form change is calculated by dividing by. In the common region specifying step, the region of the observation target on the observation image at the predetermined time is overlapped with the region of the observation target on the other observation image at a time before the predetermined time. The program according to any one of claims 1 to 4, wherein a common area of the observation target is specified. In the common region specifying step, the observation target region is overlapped so that a centroid of the observation target region on the observation image and a centroid of the observation target region on the other observation image overlap. The program according to claim 5. In the common area specifying step, the center of the figure circumscribing the observation target area on the observation image and the center of the figure circumscribing the observation target area on the other observation image overlap. The program according to claim 5, wherein the regions to be observed are overlapped. In the calculation step, based on the degree of the form change, it is determined whether or not the observation target has changed in form,
Processing that processes the observation image so that the observation object whose form has changed on the observation image and the observation object whose form has not changed are displayed in different display formats based on the determination result in the calculation step The program according to any one of claims 1 to 7, further causing the computer to execute processing steps. In the calculation step, for a plurality of observation images at different times, it is determined whether or not the observation target on the observation image has changed in form based on the degree of change in form.
A processing step of generating data indicating the number of the observation objects whose form has changed on the observation image at each time and the number of the observation objects whose form has not changed based on the determination result in the calculation step; The program according to any one of claims 1 to 7, wherein the program is executed by a computer. The computer further executes a processing step for generating data indicating a degree of the shape change of the observation target on the observation image at each time. The listed program. In the processing step, for the observation object observed under an observation condition to which a different drug is added, data indicating the degree of form change is generated for each observation condition, and based on the data indicating the degree of form change The program according to claim 10, wherein the effect of the drug added under different observation conditions is determined. 12. The computer is further configured to execute a culture condition determining step of determining a culture condition of the cell based on a degree of the morphological change of the cell as the observation target. The program as described in any one of. Detecting means for detecting the region of the observation target from the observation image of the observation target whose form changes over time;
A common area specifying means for specifying an area common to the observation target area on the observation image at a predetermined time and the observation target area on another observation image at a time before the predetermined time;
The area of the common area of the observation target specified by the common area specifying means, the area of the observation target area on the observation image at the predetermined time, and the area of the observation target on the other observation image An image processing apparatus comprising: an arithmetic unit that calculates the degree of change in shape of the observation target using at least one of the areas. The calculation means calculates the area of the observation target common area from the observation target area on the observation image at the predetermined time and the observation target common area on the other observation image. The image processing apparatus according to claim 13, wherein the degree of the shape change is calculated by dividing by an area of a region including the region to be observed. The calculation means calculates the area of the region that is not common to the region to be observed on the observation image at the predetermined time and the region to be observed on the other observation image as the observation image at the predetermined time. The image processing apparatus according to claim 13, wherein the degree of form change is calculated by dividing by an area of the region to be observed above. The calculation means calculates the area of the region that is not common to the region of the observation target on the observation image at the predetermined time and the region of the observation target on the other observation image as the area of the common region of the observation target. The image processing apparatus according to claim 13, wherein the degree of the form change is calculated by dividing by. The common area specifying unit superimposes the observation target area on the observation image at the predetermined time and the observation target area on the other observation image at a time before the predetermined time. The image processing apparatus according to claim 13, wherein a common area of the observation target is specified. The common area specifying means superimposes the observation target area so that a centroid of the observation target area on the observation image and a centroid of the observation target area on the other observation image overlap. The image processing apparatus according to claim 17. The common area specifying unit is configured such that a center of a figure circumscribing the observation target area on the observation image overlaps a center of a figure circumscribing the observation target area on the other observation image. The image processing apparatus according to claim 17, wherein the regions to be observed are overlapped. The computing means determines whether or not the observation object has undergone a morphological change based on the degree of the morphological change,
Processing that processes the observation image so that the observation object whose form has changed on the observation image and the observation object whose form has not changed are displayed in different display formats based on the determination result by the computing means The image processing apparatus according to claim 13, further comprising: means. The arithmetic means determines whether or not the observation target on the observation image has undergone morphological change based on the degree of morphological change for the plurality of observation images at different times,
Based on the determination result by the computing means, further includes processing means for generating data indicating the number of the observation objects whose form has changed on the observation image at each time and the number of the observation objects whose form has not changed. The image processing apparatus according to claim 13, wherein the image processing apparatus is an image processing apparatus. The image processing according to any one of claims 13 to 19, further comprising processing means for generating data indicating a degree of the shape change of the observation target on the observation image at each time. apparatus. The processing means generates data indicating the degree of the morphological change for each of the observation conditions for the observation target observed under the observation conditions to which different drugs are added, and based on the data indicating the degree of the morphological change. The image processing apparatus according to claim 22, wherein the effect of the drug added under the different observation conditions is determined. 24. The culture condition determining means for determining the culture condition of the cell based on the degree of the morphological change of the cell, wherein the observation target is a cell. The image processing apparatus according to one item. From the observation image of the observation object whose form changes with the passage of time, a detection step for detecting the region of the observation object;
A common area specifying step of specifying an area common to the observation target area on the observation image at a predetermined time and the observation target area on another observation image at a time before the predetermined time;
The area of the common area of the observation target specified by the processing of the common area specifying step, the area of the observation target area on the observation image at the predetermined time, and the observation target on the other observation image An image processing method comprising: a calculation step of calculating a degree of change in shape of the observation target using at least one area of the region. In the calculation step, the area of the common area of the observation target is determined by subtracting the common area of the observation target from the observation target area on the observation image at the predetermined time and the other observation image. 26. The image processing method according to claim 25, wherein the degree of the shape change is calculated by dividing by an area of a region including the region to be observed. In the calculation step, the area of the region that is not common to the region to be observed on the observation image at the predetermined time and the region to be observed on the other observation image is defined as the observation image at the predetermined time. 26. The image processing method according to claim 25, wherein the degree of form change is calculated by dividing by the area of the region to be observed above. In the calculation step, the area of the observation target area on the observation image at the predetermined time and the area of the observation target area on the other observation image that is not common are the areas of the observation target common area. 26. The image processing method according to claim 25, wherein the degree of form change is calculated by dividing by. In the common region specifying step, the region of the observation target on the observation image at the predetermined time is overlapped with the region of the observation target on the other observation image at a time before the predetermined time. The image processing method according to any one of claims 25 to 28, wherein a common area of the observation target is specified. In the common region specifying step, the observation target region is overlapped so that a centroid of the observation target region on the observation image and a centroid of the observation target region on the other observation image overlap. 30. The image processing method according to claim 29. In the common area specifying step, the center of the figure circumscribing the observation target area on the observation image and the center of the figure circumscribing the observation target area on the other observation image overlap. 30. The image processing method according to claim 29, wherein regions to be observed are overlapped. In the calculation step, based on the degree of the form change, it is determined whether or not the observation target has changed in form,
Processing that processes the observation image so that the observation object whose form has changed on the observation image and the observation object whose form has not changed are displayed in different display formats based on the determination result in the calculation step The image processing method according to any one of claims 25 to 31, further comprising a processing step. In the calculation step, for a plurality of observation images at different times, it is determined whether or not the observation target on the observation image has changed in form based on the degree of change in form.
A processing step of generating data indicating the number of the observation objects whose form has changed on the observation image at each time and the number of the observation objects whose form has not changed based on the determination result in the calculation step; 32. The image processing method according to claim 25, further comprising: 32. The image according to any one of claims 25 to 31, further comprising a processing step of generating data indicating a degree of the shape change of the observation target on the observation image at each time. Processing method. In the processing step, for the observation object observed under an observation condition to which a different drug is added, data indicating the degree of form change is generated for each observation condition, and based on the data indicating the degree of form change The image processing method according to claim 34, wherein the effect of the drug added under different observation conditions is determined. The cell to be observed is a cell, and the culture condition of the cell is determined based on the degree of the morphological change calculated by the image processing method according to any one of claims 25 to 35. Cell growth method.

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