Image processing method, program, and recording medium

Abstract

To accurately separate, from a three-dimensional image on or more cancer cell spheroids, regions corresponding to one or more cell masses and regions corresponding to protrusions.SOLUTION: An image processing method according to the present invention comprises the steps of: extracting, from an original image, object regions occupied by objects corresponding to cancer cell spheroids, and creating a binarized image in which the object regions are binarized to have higher luminance than other regions; creating a distance image by distance-transforming the binarized image and assigning to each pixel in the object region a luminance value so that the longer a distance from the pixel to the nearest outer edge of the object region is, the higher the luminance value of the pixel is; performing a maximum value filtering process on the distance image to create a post-filter image; and regionally segmenting the object regions in the post-filter image, so that regions of higher luminance than a predetermined threshold are recognized as regions corresponding to cell masses and regions of lower luminance than the threshold are recognized as regions corresponding to protrusions.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to an image processing method for analyzing an image obtained by imaging a cell mass, and particularly relates to a technique for separating the region of the cell mass and the region of protrusions from an image of a cancer cell spheroid.

Background Art

[0002] In the fields of cancer research and drug discovery screening, cancer cells are artificially cultured to produce cancer cell spheroids, which are then observed and evaluated. In particular, it is known that protrusions called invasive protrusions are formed in highly malignant cancer cells, and it has been observed that invasive protrusions extending radially from spheroids are formed even in an artificial culture environment mimicking the in-vivo environment. Changes in the number and length of invasive protrusions can be used as an index for evaluating the efficacy of chemical substances against cancer cells.

[0003] So far, the measurement of invasive protrusions has been performed using an image of a cancer cell spheroid obtained by an optical microscope. However, since a spheroid having a three-dimensional structure is observed as a two-dimensional image, the measurement accuracy has not been sufficient. To address this problem, for example, in Patent Document 1, a tomographic image is obtained by imaging a sample to which a fluorescent label corresponding to a protrusion is attached with a confocal microscope, and a three-dimensional image is constructed from tomographic images of a large number of cross-sections to perform measurement of the protrusion, thereby proposing a method for evaluating the protrusion-forming ability of cells.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the above prior art, an operation of fluorescently staining a sample is required. In addition, since cancer cell spheroids are usually cultured in a gel-like medium, autofluorescence of the gel occurs when a fluorescent reagent is added, which may be a factor inhibiting observation. In addition, variations in the degree of staining may affect quantitative evaluation results.

[0006] Here, for example, by using optical coherence tomography (OCT) technology, it is possible to obtain a three-dimensional image without staining the sample. As a result, the staining operation becomes unnecessary, and variations in evaluation due to staining variations can also be suppressed. On the other hand, in OCT images, cell masses and protrusions constituting cancer cell spheroids are not distinguished. Therefore, in order to perform quantitative measurement, it is necessary to separate these structures from each other in the image.

[0007] For example, there is also general-purpose image processing application software that has a function of separating such a dendritic structure from an object to be imaged and performing quantitative measurement. However, in the knowledge of the inventors of the present application, this type of application is not sufficient in terms of accuracy for the purpose of extracting protrusions of cancer cell spheroids. That is, generally, morphological processing involving contraction and dilation of image objects is applied to separate dendritic structures. Here, the root part of the protrusion in cancer cell spheroids is particularly thick, and the boundary with the cell mass is unclear. Therefore, in general morphological processing, this part is recognized as part of the cell mass, and there are cases where the number and length of the protrusions are underestimated compared to the original.

[0008] Thus, an image processing technology that enables separation and quantitative measurement of protrusions corresponding to invasive protrusions from three-dimensional images of cancer cell spheroids has not been put into practical use at present.

[0009] The present invention has been made in view of the above problems, and an object thereof is to provide an image processing technique capable of accurately separating a region corresponding to a cell mass and a region corresponding to a protrusion from a three-dimensional image of a cancer cell spheroid.

Means for Solving the Problems

[0010] One aspect of the present invention is an image processing method for identifying a region corresponding to a cell mass and a region corresponding to the protrusion from a raw image including a three-dimensional image of a cancer cell spheroid having protrusions. To achieve the above object, from the raw image, an object region occupied by an object corresponding to the cancer cell spheroid is extracted, and a binarized image is created by binarizing so that the object region has a higher luminance than other regions. A step of performing distance conversion on the binarized image to create a distance image in which luminance values are assigned to each pixel in the object region such that pixels having a greater distance to the closest outer edge of the object region have higher luminance values; a step of performing maximum value filter processing on the distance image to create a post-filter image; and a step of region-dividing from the object region a region having a luminance higher than a predetermined threshold among the object regions in the post-filter image as the region corresponding to the cell mass.

[0011] In the invention configured as described above, it becomes possible to accurately separate the cell mass and the protrusion. In particular, misrecognition of the thick part at the base of the protrusion as part of the cell mass, which has been a problem in conventional image processing applications, is suppressed. The reason for this will be described in detail later, but generally it is as follows.

[0012] The erosion (shrinkage) process used in general morphological processing shrinks the outer edges of objects in an image, and has little effect on the inside of thick objects such as invadopodia in cancer cell spheroids. Therefore, unless the outer edges are significantly shrunk, the base of the protrusion is treated as one part of the cell mass area. As an alternative process, the present invention performs distance conversion processing. Therefore, even if it is the inner area of ​​the thick part at the base of the protrusion, the brightness value assigned is relatively small compared to the area near the center of the cell mass, which is a structure much larger than the protrusion.

[0013] Furthermore, in the present invention, maximum value filtering is performed as a process corresponding to the dilation (expansion) process in conventional morphology processing. As a result, the brightness values ​​of the pixels on the outer edge of the cell cluster increase, while the brightness values ​​of the pixels in the protrusions far from the center of the cell cluster do not increase so much. As a result, the brightness values ​​of each pixel are polarized between the inside of the cell cluster and the inside of the protrusions.

[0014] Therefore, by introducing an appropriate threshold value for the brightness value of each pixel, the object region can be divided into a region corresponding to a cell cluster and a region corresponding to a protrusion. Even inside a thick protrusion, if it is far from the center of the cell cluster, the brightness value is low and it is recognized as part of the protrusion, so that erroneous recognition as in the conventional technology can be avoided.

[0015] In this way, the image processing of the present invention is configured to fully utilize the morphological characteristics specific to cancer cell spheroids having protrusions, and this configuration makes it possible to accurately separate the cell mass region from the protrusion region.

[0016] Another aspect of the present invention is a program for causing a computer to execute each step of the above-described image processing method. Another aspect of the present invention is a computer-readable recording medium that non-temporarily stores the above program. In the invention configured as described above, for example, an existing computer device can be used as the execution subject of the present invention.

Effect of the Invention

[0017] As described above, according to the present invention, it is possible to accurately separate the region of the cell mass and the region of the protrusion from a three-dimensional image of cancer cell spheroids, for example, an OCT image.

Brief Description of the Drawings

[0018]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Mode for Carrying Out the Invention

[0019] FIG. 1 is a diagram showing a configuration example of a computer device capable of executing an image processing method according to the present invention. The image processing according to the present invention can be realized by implementing a control program for executing each processing step of the image processing described below in a computer device 1 having a hardware configuration as shown in FIG. 1, for example, and causing the computer device 1 to execute the control program.

[0020] The computer device 1 has a general hardware configuration as a personal computer, for example, and includes a CPU (Central Processing Unit) 10, a memory 14, a storage 15, an input device 16, a display unit 17, an interface 18, and a disk drive 19, etc.

[0021] The CPU 10 realizes, software-wise, an image processing unit 11 as a functional block for executing the image processing described below by executing a control program prepared in advance. Note that dedicated hardware for realizing the image processing unit 11 may be provided. The memory 14 temporarily stores various data generated in the calculation process of the CPU 10. The storage 15 stores, in addition to the control program to be executed by the CPU 10, image data of the original image to be processed, image data after processing, etc. for a long term.

[0022] The input device 16 is for receiving an instruction input from an operator and includes, for example, a mouse, a keyboard, etc. The display unit 17 is, for example, a liquid crystal display having a function of displaying an image, and displays various information such as the original image, the image after processing, and a message to the operator. Note that a touch panel in which the input device and the display unit are integrated may be provided.

[0023] Interface 18 performs various data exchanges with an external device via a telecommunications line. The disk drive 19 accepts an external recording disk 2 on which various data such as image data and control programs are recorded. The image data, control programs, etc. stored on the recording disk 2 are read by the disk drive 19 and stored in the storage 16. The disk drive 19 may have a function of writing data generated within the computer device 1 to the recording disk 2.

[0024] Regarding the control program for causing the computer device 1 to execute the image processing of the present embodiment, the disk drive 19 may access and read the recording disk 2 on which this is recorded, or it may be provided from an external device via the interface 18. The same applies to the original image data.

[0025] Hereinafter, the specific content of the image processing method executed by the computer device 1 will be described. Note that this computer device 1 does not have an imaging function, and for the image to be processed, an image captured by another device is acquired from the outside, for example, through a telecommunications line. However, for example, a control program may be installed in the control unit of a device having an imaging function, and both imaging and image processing may be executed by this imaging device.

[0026] The image processing in this embodiment is a process for analyzing a three-dimensional image obtained by imaging a cancer cell spheroid to separate the region of the cell mass and the region of protrusions called invasive protrusions extending around it. The ultimate purpose is to obtain quantitative information such as the number, length, thickness, and degree of branching of the invasive protrusions.

[0027] FIG. 2 is a diagram illustrating a cancer cell spheroid that is the analysis target of the present embodiment. FIG. 2(a) is an example image of an artificially cultured cancer cell spheroid. More specifically, FIG. 2(a) is an example of an image obtained by performing tomographic imaging of a cancer cell spheroid cultured until invasion protrusions are formed in a culture environment simulating the in-vivo environment using an imaging device of an Optical Coherence Tomography (OCT) method, and creating a three-dimensional image of the cancer cell spheroid from a plurality of tomographic images. Further, FIG. 2(b) is a schematic diagram simply showing the structure of the cancer cell spheroid.

[0028] As shown in these figures, in the cancer cell spheroid S, a cell mass C in which a large number of cells gather in a substantially spherical shape at the center and protrusions P extending radially from the surface thereof are formed. Further, the protrusions may be branched in various ways. The cancer cell spheroid artificially produced in this way may be used as a model cell in drug discovery screening for the development of, for example, cancer therapeutic agents. That is, the efficacy of a chemical substance as a drug candidate can be evaluated by administering the chemical substance to the cancer cell spheroid and examining its changes.

[0029] Regarding the changes in the cancer cell spheroid, for example, they can be evaluated by measuring changes in the size and shape of the cell mass and invasion protrusions. In order to enable such evaluation, there is a need to individually specify the region occupied by the cell mass C and the region occupied by the protrusion P from the three-dimensional image of the cancer cell spheroid. The image processing of the present embodiment is directed to this.

[0030] Figure 3 is a flowchart showing an embodiment of the image processing method according to the present invention. This process is realized by the CPU 10 of the computer device 1 executing a control program that has been read from the recording disk 2 in advance and recorded in the storage 16. First, an image including a three-dimensional image of a cancer cell spheroid in which infiltration protrusions are formed is acquired as the original image to be processed (step S101). The image may be newly captured, or the image data that has been captured and saved in advance may be called.

[0031] The original image is, for example, a three-dimensional image created from a tomographic image obtained by OCT imaging as shown in Fig. 2(a). However, any method that can obtain a three-dimensional image of a cancer cell spheroid in the same way is acceptable, and the imaging method is not particularly limited.

[0032] From the original image thus obtained, an image object corresponding to the cancer cell spheroid is extracted (step S102), and based on the result, the original image is binarized by giving different luminance values to the region occupied by the image object and the other regions (step S103). Briefly, for example, the maximum luminance can be given to the region of the image object and the minimum luminance (e.g., zero) can be given to the other regions.

[0033] For the extraction of the image object, for example, the semantic segmentation method can be preferably applied. However, it is not limited to this, and various methods for regionally dividing an image based on the luminance value of each pixel can be applied. Also, when a predetermined threshold value is set for the luminance value of each pixel and a process of extracting a region with a higher or lower luminance than the threshold value is performed, the extraction result itself is a binarized image.

[0034] In the image binarized in this way, the region of the cell mass and the region of the protrusion are not separated. Also, there may be objects other than the cancer cell spheroids contained in the sample. Therefore, image processing for separating these is sequentially executed.

[0035] Specifically, first, distance transformation processing is performed on the binary image (step S104). The distance transformation processing is a general image processing that can also be executed by general-purpose image processing software. It represents each pixel in the image within the image object by the distance to the nearest black (i.e., pixel with a luminance value of zero) pixel. For example, the Distance Transform function provided in the OpenCV (Open Source Computer Vision) library can be used to perform the distance transformation of the image.

[0036] Due to the nature of the distance transformation processing, in the transformed image, pixels closer to the center of a large image object are given higher values, while pixels within a small image object or pixels close to the edge of the image object have smaller values. Outside the image object, the value is zero.

[0037] For the image after distance transformation, maximum value filter processing is performed on the luminance value (step S105). As will be described later, it is preferable that the window size in the filter processing is determined according to the size of the protrusion in the original image. At this point, as the window size, for example, a preset initial value can be applied. By performing the maximum value filter processing, the luminance value of each pixel is replaced with the luminance value of the pixel with the highest luminance among the surrounding pixels.

[0038] Then, by regionally dividing the image after the maximum value filter processing based on a predetermined threshold (step S106), the image object is divided into a region corresponding to the cell mass and a region corresponding to the protrusion. Through the processing up to this point, it has become possible to roughly divide the object in the image into a region corresponding to the cell mass and a region corresponding to the protrusion. The subsequent processing in steps S107 and later is for adjustment to optimize the processing parameters to obtain a better division result. This processing will be described later.

[0039] FIG. 4 is a diagram showing changes in the image accompanying the processing so far. Although the actual image processing is performed in a three-dimensional image space based on three-dimensional image data, so-called voxel data, for the sake of easy explanation on the paper surface, here, for convenience, one of the tomographic images that is the source of the three-dimensional image data will be used for explanation. The same applies to FIGS. 5 and 6 to be described later. Hereinafter, the image object will simply be referred to as an "object".

[0040] The image Ia shown in FIG. 4(a) is an example of an original image, specifically, one tomographic image obtained by OCT imaging of a cancer cell spheroid. In FIG. 4(a), there is a large object corresponding to the cancer cell spheroid in the center of the image. It is presumed that the large central region corresponds to the cell mass and the peripheral elongated region corresponds to the protrusion.

[0041] Several small isolated objects can be seen around the large object. Such isolated objects may include cases where they are actually isolated in the sample and cases where they are actually continuous with the central object but appear isolated in the image of a single cross-section. That is, it should be noted that there are cases where the protrusions that are continuous with the cell mass in the three-dimensional image appear isolated in the cross-sectional image. This is the same in the following images.

[0042] The image Ib shown in FIG. 4(b) is an example of the image after the binarization process (step S103) of the image Ia. In the binarized image Ib, the region extracted as the image object is shown in white (i.e., high luminance), and the other regions are shown in black (i.e., low luminance).

[0043] Also, the image Ic shown in FIG. 4(c) is an example of an image (i.e., a distance image) after distance conversion (step S104) of the image Ib, and the higher the value given by the processing, the higher the brightness. It is particularly bright at the center of a large object and darker towards the periphery. In a small object, since the distance to the periphery is not large, the central part is not so bright either.

[0044] When compared with the binary image Ib, the object appears to shrink and the parts with particularly fine structures become thinner, and in that sense, it is similar to the erosion process in conventional morphological processing. However, unlike the erosion process that shrinks the object itself, the size of the object has not changed, and only the brightness of its periphery has decreased.

[0045] The image Id shown in FIG. 4(d) is an example of an image after performing maximum value filter processing (step S105) on the image Ic after distance conversion. By the maximum value filter processing, the high-brightness region is enlarged compared to the image Ic. The low-brightness region at the periphery is also enlarged by an amount corresponding to the window size by the maximum value filter processing. In this sense, it can also be said to be a process substituting for the dilation process in conventional morphological processing.

[0046] FIG. 5 is a diagram for explaining the principle of region division in the present embodiment. As a simplified model of a cancer cell spheroid, as shown in FIG. 5(a), a structure in which a relatively thin protrusion P1 and a thicker protrusion P2 extend in opposite directions from a substantially circular cell mass C is used to explain the principle of region division in the present embodiment.

[0047] Figure 5(b) shows the luminance distribution in the image after distance conversion processing. Specifically, it shows the luminance distribution along the line drawn in the direction in which the protrusions P1 and P2 extend through the center of the cell mass C, as indicated by the dotted line in Figure 5(a). As can be seen from this, the luminance is highest at the center of the cell mass C and decreases as it approaches the peripheral part. If there were no protrusions, as indicated by the dotted line in Figure 5(b), the luminance would decrease until it becomes zero at the peripheral part of the cell mass C. However, the presence of the protrusions P1 and P2 suppresses the decrease in luminance at the peripheral part of the cell mass C. Also, at the protrusions P1 and P2, the luminance value is high at the wide part and decreases as it approaches the thin tip part.

[0048] Figure 5(c) shows the luminance distribution on the same line as above in the image after maximum value filter processing. By performing the maximum value filter processing, the profile after distance conversion shown in Figure 5(b) (indicated by the dotted line in Figure 5(c)) will spread by a width corresponding to the window size W as indicated by the solid line. As can be seen from this figure, in the region corresponding to the cell mass C, since the luminance inside is high, the luminance increases relatively greatly after the filter processing. Especially at its peripheral part, the increase is remarkable. In contrast, in the region corresponding to the protrusions P1 and P2 which have a lower luminance value because they are structures thinner than the cell mass C, the increase in luminance is slight. This difference is an important factor for accurately separating the cell mass and the protrusions.

[0049] Here, as shown in Figure 5(d), an appropriate threshold Th is introduced, and region division is performed by considering the region where the luminance value is higher than the threshold Th as the cell mass C and the region where the luminance value is lower than the threshold Th as the region corresponding to the protrusions P1 and P2. Then, as is clear from the comparison between Figure 5(a) and Figure 5(d), these regions will be separated from each other at a position close to the boundary between the actual cell mass C and the protrusions P1 and P2. Naturally, since the boundary between the divided regions varies greatly depending on the threshold, it is a necessary condition that the threshold be set appropriately.

[0050] As can be seen from FIG. 5(c), when performing division based on a threshold value on a profile (dotted line) that does not perform maximum value filter processing, the region corresponding to the cell mass C is estimated to be smaller than the actual one. As shown by the solid line in FIG. 5(c), by performing maximum value filter processing and selectively increasing the luminance value inside the cell mass C, particularly at its peripheral part, such a problem can be avoided.

[0051] In the image processing of this embodiment, in order to separate the fine structure corresponding to the protrusion from the region corresponding to the cell mass, distance conversion processing is executed. According to this method, the weight of the peripheral part becomes relatively smaller compared to the central part of the object, but different from the erosion processing in the conventional method, a part of the object is not erased. That is, the information for specifying the contour of the object is maintained at this point.

[0052] Then, maximum value filter processing is performed as a process corresponding to the dilation processing in the conventional method. The effect of increasing the luminance value by this is large in the cell mass which is a relatively large structure and has reached a high luminance value by distance conversion processing, while it is small in the protrusion which is a smaller structure. Therefore, the luminance value increases significantly at the peripheral part of the cell mass, but the increase in the luminance value is limited at the peripheral part of the protrusion.

[0053] Therefore, in the subsequent region division processing, it is possible to extract, as a region corresponding to the cell mass, a region with a luminance higher than a appropriately set threshold value. That is, since the cell mass is given a relatively high luminance value up to the pixels at its peripheral part, it is possible to distinguish it from the protrusion where the effect of increasing the luminance value by the maximum value filter processing is small by a simple comparison with the threshold value. Note that due to the maximum value filter processing, the peripheral part of the protrusion expands somewhat. Therefore, if a region with a luminance lower than the threshold value is regarded as the region of the protrusion as it is, a region larger than the actual one will be extracted. To prevent this, it is desirable to handle, as the region of the protrusion, a region obtained by deleting the region regarded as the region corresponding to the cell mass from the image before the maximum value filter processing, for example, the original image or the binarized image.

[0054] As described above, the combination of the distance conversion process and the maximum value filter process in the present embodiment clarifies the luminance contrast between the cell mass and the protrusion, and enables the appropriate separation of the cell mass and the protrusion by subsequent region division based on a threshold value.

[0055] FIG. 5(e) schematically shows, as a comparative example, the result of the erosion process in region division by the combination of the conventional erosion process and dilation process. By uniformly shrinking the peripheral part of the object by an appropriate amount, it is relatively easy to eliminate the part corresponding to the relatively thin protrusion P1 from the object. On the other hand, for the wide protrusion P2, it remains as a part of the object, which is the cause of the above-mentioned problem of the prior art, that is, the root part of the thick protrusion is regarded as a part of the cell mass.

[0056] In principle, if the amount of shrinkage is increased, it is possible to eliminate such protrusions. However, if the amount of shrinkage is too large, a large amount of information regarding the original shape will be lost, and the reproducibility of the object shape after the dilation process will be significantly reduced.

[0057] In contrast, in the image processing of the present embodiment, by performing binarization processing, distance conversion processing, and maximum value filter processing on the original image in order, and finally performing region division based on a threshold value, it is possible to suppress the above-mentioned problems.

[0058] FIG. 6 is a diagram showing an example of the result of region division. FIG. 6(a) is an example of an image showing, in white, the region determined to correspond to the cell mass C in the binarized image Ib. FIG. 6(b) is an example of an image showing, in white, the region determined to correspond to the protrusion P in the binarized image Ib. For example, by acting these on the original image Ia as mask images, it becomes possible to extract the region corresponding to the cell mass C and the region corresponding to the protrusion P from the original image Ia.

[0059] Returning to FIG. 3, the description of the image processing of this embodiment will be continued. By the processing up to step S106, the object is divided into a region corresponding to the cell mass C and a region corresponding to the protrusion P. However, if the processing parameters, specifically the window size W in the maximum value filter processing and the threshold Th in the region division processing, are not set appropriately, the division result will not be good. Therefore, the image processing of this embodiment is configured to be able to adjust these processing parameters as necessary.

[0060] Specifically, the result image obtained by the processing up to step S106 is displayed on the display unit 17 and presented to the user (step S107), and an operation input from the user regarding parameter change is received. When the user wishes to change the setting of the threshold for region division (YES in step S108), a new threshold setting input is received (step S211), the region division processing is executed based on the new threshold (step S106), and the result is displayed (step S107). Also, when the user wishes to change the setting of the window size in the maximum value filter processing (YES in step S109), the new setting input is received (step S212), the maximum value filter processing is re-executed based on the new window size (step S105). Further, the region division processing is re-executed (step S106), and the result is displayed (step S107).

[0061] When the user does not wish to change the parameters (NO in both steps S108 and S109), the result image at that time is stored and saved in the storage 15 (step S110), and the processing ends. In this way, the parameters are optimized, and it becomes possible to obtain the division result desired by the user.

[0062] Regarding the window size W among these parameters, for the purpose of separating protrusions from cell clusters, it is possible to estimate its optimal value to some extent from the size of the protrusions. Specifically, it has been found that about (1 / 2) times to 1 time the thickness of the protrusion is suitable. Note that the actual maximum value filtering process is a spherical filtering process in a three-dimensional image space. Therefore, the window size W referred to here is a concept corresponding to the diameter of the spherical window in the spherical filtering process. The radius of the spherical window may be regarded as the window size.

[0063] In cancer cell spheroids, invasive protrusions can have various thicknesses. In this case, as the "thickness of the protrusion" related to the determination of the window size, for example, the thickness (width) of the thickest part among various protrusions can be used. Thus, regarding the window size in the maximum value filtering process, it is possible to narrow it down to some extent when the outer shape of the cancer cell spheroid can be grasped. That is, it is considered that there is no need to frequently change the window size when optimizing the parameters.

[0064] On the other hand, the threshold Th is an important parameter for defining the boundary between the cell cluster and the protrusion, and fine adjustment is required. For this reason, in the process shown in FIG. 3, a processing loop related to the adjustment of the threshold is provided inside the processing loop related to the adjustment of the window size. Also, as shown below, a user interface screen (Graphical User Interface; GUI) for assisting the user in adjusting the threshold is provided.

[0065] FIG. 7 is a diagram showing an example of a GUI screen suitable for adjusting the threshold. FIGS. 7(a) to 7(d) are all examples of the result images of the region division in step S106. Among the regions occupied by the objects in the image, the region regarded as corresponding to the cell cluster is colored to be brighter (whiter on the paper surface) than the region regarded as the protrusion.

[0066] In each of these images, the image Ip subjected to the above-described coloring process is accompanied by a histogram H of the luminance values in the image and a gauge G representing the set value of the threshold Th for the luminance values. From the comparison of these images, it can be seen that the boundary between the cell mass and the protrusion changes significantly depending on the setting of the threshold Th.

[0067] The user can change the setting of the threshold Th by operating the gauge G via the input device 16. Each time the threshold is changed, the region division is re-executed, and the resulting image is displayed each time. The user can adjust the threshold Th while checking the resulting image, and it becomes possible to find the optimal threshold Th in a short time.

[0068] FIG. 8 is a diagram showing the effect of region division according to the present embodiment. FIG. 8(a) is an image in which the resulting image of the image processing of the present embodiment is applied to the original three-dimensional image to distinguish the region occupied by the cell mass and the region occupied by the protrusion in the three-dimensional image. Here, an image obtained by projecting the three-dimensional image onto a two-dimensional plane is shown. Specifically, the darker-colored portion in the central part is the region regarded as the cell region, and the whiter region is the region regarded as the protrusion. It can be said that the region division is accurately performed between the region of the cell mass in the central part and the region of the protrusion extending radially outward from the surface thereof.

[0069] FIG. 8(b) is shown as a comparative example, and shows the result of dividing the region of the cell mass and the region of the protrusion by a combination of the conventional erosion process and dilation process. Even if the shrinkage amount in erosion and the dilation amount in dilation are adjusted sufficiently, as indicated by the arrows in the figure, a region misrecognized as the cell mass remains in a part of the particularly thick protrusion.

[0070] As described above, in the image processing of this embodiment, for the three-dimensional image of cancer cell spheroids, each process of binarization processing, distance conversion processing, maximum value filter processing, and region division based on a threshold is performed in order based on the object extraction result. As a result, it is possible to separate the region of the cell mass and the region of the protrusion (invasive protrusion) with higher accuracy than the conventional method.

[0071] If the cell mass and the invasive protrusion are accurately separated in this way, the results can be used for various quantitative evaluations, for example, evaluations such as the number, thickness, and length of the invasive protrusions and the degree of their branching. For example, if an image is created by removing the image of the cell mass from the three-dimensional image and leaving only the image of the protrusion, measurements for such evaluations can be easily performed based on the image. In particular, according to this embodiment, since misrecognition of thick invasive protrusions as cell masses is reduced, it is possible to avoid the problem that the length of the invasive protrusion is underestimated compared to the actual length.

[0072] Note that the present invention is not limited to the above-described embodiment, and various modifications can be made other than those described above without departing from the spirit of the invention. For example, the computer device 1 of the above embodiment has a control program for executing the image processing according to the present invention pre-installed. That is, the present invention may be implemented as a software program for causing a computer device to execute each processing step of the above-described processing.

[0073] Distribution of such a program can be performed, for example, in a form of downloading via a telecommunication line such as the Internet, and can also be performed by distributing a computer-readable recording medium on which the program is recorded. Also, for example, by causing an existing microscope imaging device to read this program via an interface, it is also possible to implement the present invention with the device.

[0074] For example, in the above embodiment, as processing parameters, it is possible to change and set the window size in the maximum value filter processing and the threshold value in the region division processing. Regarding the window size among these, as described above, it is possible to estimate an appropriate value to some extent from the size (thickness) of the invasive protrusions in the cancer cell spheroid to be analyzed. Therefore, it may be a mode of using a fixed value determined in advance according to the size of the object or allowing only the initial value to be set by the user.

[0075] In addition to the image processing of the above embodiment, in order to eliminate images of minute objects other than the object to be analyzed and image noise, other image processing such as noise removal processing and smoothing processing may be appropriately combined and executed.

[0076] In the image processing of the above embodiment, a binary image in which the inside of the image object in the three-dimensional image has high luminance and the background has low luminance is used. In the distance conversion processing, distance conversion is performed so that the center part of the object has higher luminance, maximum value filter processing is performed, and in the region division processing, a region with luminance higher than the threshold value is regarded as the region of the cell mass. However, technically, it is equivalent to reverse the luminance relationship in these processes.

[0077] In addition, the image processing of the above embodiment analyzes the three-dimensional image of the cancer cell spheroid obtained by OCT imaging. However, the imaging method is not limited to this, and images obtained by various imaging methods capable of three-dimensionally expressing the three-dimensional shape of the surface of the object to be analyzed can be processed in the same way. Also, the image processing of this embodiment can be applied not only to cancer cell spheroids but also to the analysis of various cell masses in which protrusions extend outward from the surface of the aggregated cell mass in the same way.

[0078] As described above by way of example of specific embodiments, in the image processing method according to the present invention, for example, in distance conversion, a luminance value higher than the luminance value given to pixels outside the object region may be assigned to each pixel within the object region. Such processing can be realized using the standard functions of general-purpose image processing software and is also sufficiently practical as the distance conversion processing of the present invention.

[0079] Also, as a method for extracting an object region from the original image, for example, a semantic segmentation method can be applied. According to such a configuration, even if the object to be extracted has an irregular shape such as a cancer cell spheroid, it is possible to accurately extract the object region.

[0080] Also, for example, a step of displaying the image after region division on a display device may be further provided. In this way, by presenting the result of the processing to the user as an image, the user can confirm the result of the processing. For example, it can be used for adjusting parameters as follows.

[0081] For example, the threshold value can be changed and set by a user operation. By setting the threshold value, the position of the boundary defined between the cell mass and the protrusion changes, and this greatly affects the quality of the separation between the two. By making the threshold value a parameter that can be changed, it is possible to more accurately separate the cell mass and the protrusion by adjusting it according to the state of the cancer cell spheroid to be analyzed.

[0082] Similarly, the window size in the maximum value filter processing may be changed and set by a user operation. The window size in the maximum value filter processing is a parameter related to the contrast of the luminance value for separating the cell mass and the protrusion between them, and particularly affects the reproducibility of their shapes after separation. By making this adjustable as needed, it is possible to more accurately separate the cell mass and the protrusion.

[0083] For example, the maximum value filter process and the region division may be configured to be repeatedly executable in this order. As described above, both the window size, which is a processing parameter related to the maximum value filter process, and the threshold value, which is a processing parameter related to the region division, are related to the accuracy of separating cell clusters and protrusions. When these are changed, it is preferable that the maximum value filter process or the region division is re-executed. If these two processes are repeatedly executed, it becomes possible to gradually approximate the two parameters to optimal values.

[0084] Further, for example, the image processing method according to the present invention may be configured to create an image showing a region corresponding to protrusions by erasing a region corresponding to cell clusters from an original image or a binarized image. According to such a configuration, a three-dimensional image showing only the region corresponding to the protrusions is created. Based on this image, it becomes possible to easily perform a quantitative evaluation of the protrusions.

[0085] For example, the original image may be an image obtained by imaging using an optical coherence tomography method. In imaging using the optical coherence tomography (OCT) method, it is possible to perform non-destructive and non-invasive imaging on cells and spheroids. Therefore, for example, by periodically imaging cultured cancer cell spheroids, it is possible to observe the temporal changes of the cancer cell spheroids. And the image obtained by OCT imaging is suitably applicable to the image processing according to the present invention.

Industrial Applicability

[0086] This invention is applicable to the quantitative evaluation of artificially cultured cancer cell spheroids and is suitable for, for example, the medical and drug discovery fields.

Explanation of Signs

[0087] 1 Computer device 2 Recording disk (recording medium) 10 CPU 15 Storage 16 Input device 17 Display unit C cell mass P, P1, P2 invasion projections (protrusion parts)

Claims

1. An image processing method for identifying a region corresponding to a cell mass and a region corresponding to the protrusion from an original image including a three-dimensional image of a cancer cell spheroid having protrusions, comprising: extracting an object region occupied by an object corresponding to the cancer cell spheroid from the original image, and creating a binarized image obtained by binarizing the image such that the object region has a higher luminance than other regions; performing distance transformation on the binarized image, and creating a distance image in which luminance values are assigned to each pixel in the object region such that pixels having a greater distance to the nearest outer edge of the object region have higher luminance values; performing maximum value filter processing on the distance image to create a post-filter image; performing region division on the object region in the post-filter image by setting a region having a luminance higher than a predetermined threshold value among the object regions as the region corresponding to the cell mass; An image processing method comprising the steps of.

2. The image processing method according to claim 1, wherein in the distance transformation, a luminance value higher than a luminance value given to a pixel outside the object region is assigned to each pixel in the object region.

3. The image processing method according to claim 1 or 2, wherein the object region is extracted from the original image by semantic segmentation.

4. The image processing method according to any one of claims 1 to 3, further comprising the step of displaying the image after region division on a display device.

5. The image processing method according to any one of claims 1 to 4, wherein the threshold value can be changed and set by a user operation.

6. The image processing method according to any one of claims 1 to 5, wherein the window size in the maximum value filter processing can be changed and set by a user operation.

7. The image processing method according to claim 5 or 6, wherein the maximum value filter processing and the region division can be repeatedly executed in this order.

8. The image processing method according to any one of claims 1 to 7, wherein an image showing the region corresponding to the protrusion is created by deleting the region corresponding to the cell mass from the original image or the binarized image.

9. The image processing method according to any one of claims 1 to 8, wherein the original image is an image obtained by imaging using an optical coherence tomography imaging method.

10. A program for causing a computer to execute each step of the image processing method according to any one of claims 1 to 9.

11. A computer-readable recording medium on which the program according to claim 10 is non-temporarily recorded.

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