Image Processing Method

Abstract

To provide an image processing method and an image processing program for generating an image that enables a more accurate grasp of the shape of a vascular network structure.SOLUTION: An image processing method includes the steps of: acquiring an image in which a cell structure having a stained vascular network structure is imaged; applying a wavelet transform to the image to generate a contour image in which contours of the vascular network structure are extracted; and repeatedly excluding pixels from object boundaries drawn in the contour image to generate a skeleton image in which skeletons having a line width of a predetermined number of pixels are extracted.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an image processing method. [Background technology]

[0002] Patent Document 1 discloses an artificial cell structure having a vascular network structure. The cells that make up the vascular network structure of this cell structure are immunostained with an anti-CD31 antibody. This makes the vascular network structure observable under a microscope. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2017 / 146124 Summary of the Invention [Problem to be solved by the invention]

[0004] Although the vascular network structure of the cell structure described in Patent Document 1 can be observed using a microscope, its dense structure makes quantitative analysis of the vascular network structure difficult. One example of a method for quantitatively analyzing the vascular network structure is to count the number of cells in the cell structure by subjecting the fabricated cell structure to trypsin treatment or the like. However, this method makes it difficult to count only the cells that make up the vascular network structure. Alternatively, it is possible to identify and evaluate the vascular network structure based on the intensity of fluorescence from the cells that make up the vascular network structure in an image of the cell structure after immunostaining. However, because the cells that are stained are not necessarily limited to those that make up the vascular network structure, this method is insufficient for evaluating the shape, such as the length, of the vascular network structure. The present disclosure provides a method and program for generating an image that allows for more accurate understanding of the shape of the vascular network structure. [Means for solving the problem]

[0005] An image processing method according to one aspect of the present invention includes the steps of acquiring an image of a cellular structure having a colored vascular network structure, applying a wavelet transform to the image to generate a contour image in which the contour of the vascular network structure is extracted, and repeatedly excluding object pixels from the boundary of the object recognized in the contour image to generate a skeleton image in which a skeleton having a line width of a predetermined number of pixels is extracted.

[0006] According to this image processing method, an image of a cellular structure having a vascular network structure is processed. First, a wavelet transform is applied to the image. This generates a contour image in which the contour of the vascular network structure is extracted. Next, object pixels are repeatedly removed from the boundary of the object recognized in the contour image. This generates a skeleton image in which the skeleton of the vascular network structure is extracted. By generating the skeleton image from the contour image, a skeleton that more accurately reflects the shape of the blood vessels can be obtained compared to when the skeleton image is generated directly from the image. Therefore, this image processing method can generate an image that can more accurately grasp the shape of the vascular network structure.

[0007] In one embodiment, the image processing method may further include detecting a shape of a vascular network structure based on the skeleton image, in which case the image processing method can evaluate characteristics of the vascular network structure using the detected shape of the vascular network structure.

[0008] In one embodiment, the shape of the vascular network structure may be the length of the vascular network structure, in which case the image processing method can evaluate the characteristics of the vascular network structure using the detected length of the vascular network structure.

[0009] In one embodiment, the captured image may be an image of a region including the center of the cell structure, in which case the image processing method can appropriately extract a region that is likely to contain the vascular network structure of the cell structure.

[0010] In one embodiment, the image processing method may further include a step of generating an image by removing background signals from the image, the step being performed after the acquiring step and before the step of generating a contour image, in which the background signals from the image are removed, so that the image processing method can more clearly extract a skeleton representing a vascular network structure.

[0011] In one embodiment, the image processing method may further include a step of generating an extracted image, which is executed after the step of generating a skeleton image, in which skeletons drawn in the skeleton image have lengths greater than a threshold value. In this case, vascular structures whose skeleton lengths are equal to or less than the threshold value are not extracted, and therefore the image processing method can remove, as noise, skeletons that are unlikely to constitute a vascular network structure.

[0012] In one embodiment, the image processing method may further include a step of converting the image into an image of a predetermined gradation, which is performed after the acquiring step and before the generating step of the contour image. In this case, since the captured image is converted into an image of the predetermined gradation, the image processing method can easily proceed with processing on the converted image.

[0013] In one embodiment, the image processing method further includes the steps of immunostaining cells having a vascular network structure, fluorescently modifying the cells having a vascular network structure in advance, or a combination of immunostaining and fluorescent modification to cause the vascular network structure to develop a color, and imaging the cellular structure having the vascular network structure that has developed a color in the coloring step, and the acquiring step may acquire an image captured in the imaging step. In this case, the image processing method performs the coloring step and the imaging step on the cellular structure having a vascular network structure, thereby making it possible to more accurately grasp the shape of the vascular network structure for each cellular structure.

[0014] In one embodiment, the cell structure may comprise vascular endothelial cells.

[0015] An image processing program according to another aspect of the present disclosure causes a computer to perform the steps of acquiring an image of a cellular structure having a colored vascular network structure, applying a wavelet transform to the image to generate a contour image in which the contour of the vascular network structure is extracted, and repeatedly excluding pixels of the object from the boundary of the object drawn in the contour image to generate a skeleton image in which a skeleton having a line width of a predetermined number of pixels is extracted.

[0016] This image processing program has the same effect as the image processing method described above. [Effects of the Invention]

[0017] According to one aspect and embodiment of the present disclosure, the shape of a vascular network structure can be grasped more accurately. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 is a block diagram showing an example of functions of an image processing system including an image processing apparatus according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the image processing device. [Figure 3] FIG. 3 is a flowchart showing an example of image acquisition processing that is the target of the image processing method according to the embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of an image processing method according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of an image acquired by the contour image generation process and the skeleton image generation process according to the embodiment. [Figure 6] FIG. 6 is a diagram showing an example of an image obtained by the tone conversion process, the noise removal process, the contour image generation process, and the skeleton image generation process. [Figure 7] FIG. 7 is a diagram showing an example of an image obtained by the pixel value adjustment process, the contour image generation process, and the skeleton image generation process. [Figure 8]FIG. 8 is a diagram showing an example of an image acquired by the extraction image generation process. [Figure 9] (a) to (f) of Figure 9 are diagrams showing examples of captured images of a cell structure administered with cyclophosphamide, and (g) to (l) of Figure 9 are diagrams showing examples of extracted images from the captured images of a cell structure administered with cyclophosphamide. [Figure 10] Figure 10(a) is a diagram showing the correspondence between the skeletal length in the extracted image of the embodiment and the dosage concentration of cyclophosphamide, and Figure 10(b) is a diagram showing the correspondence between the number of branching points in the skeletal structure in the extracted image and the dosage concentration of cyclophosphamide. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, an embodiment will be described with reference to the drawings. In the following description, the same or equivalent elements are designated by the same reference numerals, and redundant description will be omitted.

[0020] [Outline of cell structure] First, we will outline the cell structure that is the subject of imaging by the image processing device according to the embodiment. The cell structure includes, for example, vascular endothelial cells, and a specific example is a three-dimensional hepatoid tissue (3D liver model). The 3D liver model is a tissue that has interstitial cells including vascular endothelial cells and can be evaluated for hepatotoxicity. The 3D liver model includes, for example, hepatocytes, and further includes at least one type of cell selected from the group consisting of fibroblasts (e.g., hepatic stellate cells), immune cells, and smooth muscle cells, as well as vascular endothelial cells, as interstitial cells. The cell structure may further include extracellular matrix components that function as a scaffold for the cells, and may also include components (e.g., polyelectrolytes) that support the formation of other cell structures.

[0021] As used herein, the term "extracellular matrix component" refers to an assembly of extracellular matrix molecules formed by multiple extracellular matrix molecules. Extracellular matrix refers to a substance present outside cells in an organism. Any substance can be used as the extracellular matrix as long as it does not adversely affect cell growth and cell aggregate formation. Specific examples include, but are not limited to, collagen, elastin, proteoglycan, fibronectin, hyaluronic acid, laminin, vitronectin, tenascin, entactin, fibrillin, and cadherin. The extracellular matrix component may be used alone or in combination. The extracellular matrix component may, for example, contain a collagen component or may be a collagen component. When the extracellular matrix component is a collagen component, the collagen component functions as a scaffold for cell adhesion, further promoting the formation of a three-dimensional cell structure. In this embodiment, the extracellular matrix component is preferably a substance present outside animal cells, i.e., an animal extracellular matrix component. In addition, the extracellular matrix molecule may be a modified or variant of the above-mentioned extracellular matrix molecule, or may be a polypeptide such as a chemically synthesized peptide, as long as it does not adversely affect cell growth and cell aggregate formation.

[0022] The extracellular matrix component may have repeats of a Gly-XY sequence, which is characteristic of collagen. Here, Gly represents a glycine residue, and X and Y each independently represent any amino acid residue. Multiple Gly-XY residues may be the same or different. Having repeats of a Gly-XY sequence reduces constraints on the molecular chain arrangement, resulting in improved scaffold function. In an extracellular matrix component having repeats of a Gly-XY sequence, the proportion of Gly-XY sequences in the total amino acid sequence may be 80% or more, preferably 95% or more. The extracellular matrix component may also have an RGD sequence. The RGD sequence refers to a sequence represented by Arg-Gly-Asp (arginine residue-glycine residue-aspartic acid residue). When an extracellular matrix component has an RGD sequence, cell adhesion is further promoted, making it even more suitable as a scaffold. Extracellular matrix components containing a sequence represented by Gly-XY and an RGD sequence include collagen, fibronectin, vitronectin, laminin, cadherin, and the like.

[0023] Examples of the shape of the extracellular matrix components include fibrous shapes. Fibrous shapes refer to shapes composed of thread-like extracellular matrix components or shapes composed of thread-like extracellular matrix components cross-linked intermolecularly. At least a portion of the extracellular matrix components may be fibrous. The shape of the extracellular matrix component refers to the shape of a single extracellular matrix component (aggregate of extracellular matrix components) observed under a microscope, and the extracellular matrix component preferably has an average diameter and / or average length as described below. Fibrous extracellular matrix components include thin thread-like materials (thin fibrils) formed by the aggregation of multiple thread-like extracellular matrix molecules, thread-like materials formed by the further aggregation of thin fibrils, and defibrillated versions of these thread-like materials. When fibrous extracellular matrix components are included, the RGD sequences in the fibrous extracellular matrix components are preserved without being destroyed, allowing the scaffold to function more effectively as a scaffold for cell adhesion.

[0024] Polyelectrolytes, which are components that support the formation of cell structures, are polymeric compounds with electrolytic properties. Examples of polyelectrolytes include glycosaminoglycans such as heparin, chondroitin sulfate (e.g., chondroitin 4-sulfate, chondroitin 6-sulfate), heparan sulfate, dermatan sulfate, keratan sulfate, and hyaluronic acid; dextran sulfate, rhamnan sulfate, fucoidan, carrageenan, polystyrene sulfonic acid, polyacrylamide-2-methylpropane sulfonic acid, and polyacrylic acid, or derivatives thereof, but are not limited thereto. The polyelectrolyte may consist of one of the above-mentioned compounds or may contain a combination of two or more of them.

[0025] The polyelectrolyte is preferably a glycosaminoglycan, more preferably at least one selected from the group consisting of heparin, dextran sulfate, chondroitin sulfate, and dermatan sulfate, and even more preferably heparin. When the cell structure contains a polyelectrolyte, excessive aggregation of extracellular matrix components can be more effectively suppressed, and as a result, cell structures with excellent responsiveness to hepatotoxic substances can be more easily obtained. When the cell structure contains heparin, this effect becomes even more pronounced.

[0026] The vascular network structure of the cell structure can be visualized by immunostaining, fluorescent labeling, or a combination of immunostaining and fluorescent labeling in the 3D liver model. Furthermore, by administering compounds such as cyclophosphamide to the 3D liver model, the degree of damage to the vascular network structure can be confirmed.

[0027] More specifically, a 3D liver model (cell structure) in which the vascular network imaged by the image processing device is colored is obtained by executing a fabrication process for fabricating the 3D liver model, a medication process for administering medication to the 3D liver model, and a coloring control process for coloring the 3D liver model. If the degree of damage to the vascular network structure is not to be observed, i.e., if a compound is not administered to the 3D liver model, the medication process does not need to be executed.

[0028] Table 1 shows an example of cultured cells cultured in the image processing system 2. The production process is performed using, for example, the methods and materials disclosed in WO 2017 / 146124 or WO 2018 / 143286. The production process of this embodiment is an example that uses the method disclosed in WO 2017 / 146124. The production process includes, for example, a first collection process for collecting cells other than the fresh human hepatocytes (PXB cells) derived from the human hepatocyte chimeric mouse shown in Table 1, a second collection process for collecting PXB cells, a mixture acquisition process for obtaining a cell mixture, a suspension acquisition process for obtaining a cell suspension, a gel formation process for forming a fibrin gel, and a model acquisition process for obtaining a 3D model.

[0029] [Table 1]

[0030] Table 2 shows examples of reagents and consumables used in the image processing system. Table 2 does not include general-purpose research materials such as disposable pipettes. In the first recovery process, for example, the operator prepares purchased human hepatic stellate cells (Lx2) and human hepatic sinusoidal endothelial cells (SEC) and measures the cell mass of each cell. In the first recovery process, for example, the operator may put frozen stocks of human hepatic stellate cells (Lx2) and human hepatic sinusoidal endothelial cells (SEC) to sleep, culture them without passaging according to the manufacturer's recommended protocol, recover them from culture flasks and petri dishes with trypsin according to standard methods, and then measure the cell mass of each cell. In the second recovery process, for example, the operator prepares purchased PXB cells and measures the cell mass of the PXB cells.

[0031] [Table 2]

[0032] In the mixture acquisition process, for example, the cells collected in the first and second collection processes are mixed so that the total cell amount per well is 30,000 cells, and the tissue cell ratios are 65% PXB cells, 25% SEC, and 10% Lx2, to obtain a cell mixture (an example of a cell structure). The cell mixture contains multiple types of cells, including vascular endothelial cells and other stromal cells.

[0033] In the suspension acquisition process, for example, a heparin-collagen solution is prepared by mixing equal parts of 1.0 mg / ml heparin solution (buffer: 100 mM Tris-HCl) and 0.3 mg / ml collagen solution (buffer: 5 mM acetate). Next, 100 μL of the prepared heparin-collagen solution is added to each cell mixture obtained in the mixture acquisition process. The cells are suspended until they are no longer visible, and then centrifuged (400 g x 2 min) to obtain a viscous mass. After the supernatant is removed from the viscous mass solution, solvent is added so that the final volume of the viscous mass solution is equal to the number of wells to be seeded x 2 μL, and the cell suspension is obtained.

[0034] In the gel formation process, for example, the cell suspension obtained in the suspension acquisition process is mixed with a 10 mg / mL fibrinogen solution and a 20 U / mL thrombin solution (solvent: HCM) on a 48-well plate. For example, the solvent added to the viscous body solution after the supernatant is removed from the viscous body solution in the suspension acquisition process may be a 20 U / mL thrombin solution (solvent: HCM). In this case, for example, droplets of the 10 mg / mL fibrinogen solution are formed, and then the cell suspension obtained in the suspension acquisition process is added to the droplets. Also, for example, 2 μL of the cell suspension obtained in the suspension acquisition process may be seeded to form droplets, and then the 10 mg / mL fibrinogen solution may be added. Note that the solvent added to the viscous body solution in the suspension acquisition process does not have to be a 20 U / mL thrombin solution (solvent: HCM). In this case, in the gel formation process, for example, a 20 U / mL thrombin solution (solvent: HCM) may be added to a droplet of a mixture of a cell suspension and a 10 mg / mL fibrinogen solution. The order in which the cell suspension, 10 mg / mL fibrinogen solution, and 20 U / mL thrombin solution (solvent: HCM) are mixed is not limited to the above and may be any order. The droplet of a mixture of a cell suspension, 10 mg / mL fibrinogen solution, and 20 U / mL thrombin solution (solvent: HCM) is left to stand in an incubator for 40 minutes to form a fibrin gel. Note that if it is not necessary to form the model into a fixed mass shape, the gel formation process may not be performed.

[0035] In the model acquisition process, for example, 0.5 mL of HCM (containing Endothelial Cell Growth Supplement) is added to each well in which fibrin gel was formed in the gel formation process, and a 3D model is obtained. The above processes complete the fabrication process. Note that in the mixture acquisition process, suspension acquisition process, gel formation process, and model acquisition process within the fabrication process, the values ​​of the total cell mass, intracellular cell ratio, reagent concentration, reagent solution volume, centrifugation time, and standing time are not limited to the above values ​​and may be changed as appropriate depending on the cell structure to be fabricated.

[0036] Next, the dosing process is performed. The 3D liver model created in the creation process is dosed by replacing the medium with one containing monoclotaline at concentrations of 2000 μM, 666 μM, 222 μM, and 74 μM on days 1 and 4. Each compound was previously dissolved in DMSO (dimethyl sulfoxide) at a high concentration and stored, and DMSO was added to the medium at a concentration of 1% at the time of dosing. Since each dosing condition contains 1% DMSO, a medium change with a medium containing only 1% DMSO was also performed at the same time to serve as a negative control. This completes the dosing step.

[0037] Next, a color development control process is performed, which includes, for example, a fixation process, a permeabilization process, a primary antibody process, and a secondary antibody process.

[0038] For fixation, on day 6, the 48-well plate is removed from the incubator, the medium is removed, and the plate is washed with PBS. After washing, 300 μL of 4% paraformaldehyde in phosphate buffer (PFA) is added to each well to fix the 3D models. After fixation, the PFA is washed away.

[0039] Next, for permeabilization, 100 μL of 0.2 (v / v)% TRITON / 1 (w / v)% BSA PBS solution (BSA solution) is added to the insert of each well, and the plate is left to stand at room temperature for 2 hours.

[0040] Next, for the primary antibody treatment, mouse anti-CD31 antibody was diluted 100-fold with BSA solution to obtain a primary antibody solution. 100 μL of this primary antibody solution was added to each well insert and allowed to stand at 4°C for 24 hours. After standing, the primary antibody solution was washed away.

[0041] Next, in the secondary antibody treatment, the secondary antibody is diluted 200-fold with BSA solution to obtain a secondary antibody solution. 100 μL of this secondary antibody solution is added to the insert of each well and allowed to stand for 1 hour at room temperature in a dark place. After standing, the secondary antibody solution is washed away, and 100 μL of PBS is added to each well. This completes the color development control process, allowing a 3D liver model in which the vascular network structure is colored to be obtained. The color development control process may be performed using, for example, a known method.

[0042] [Image Processing System] Fig. 1 is a block diagram showing an example of the functions of an image processing system including an image processing device according to an embodiment. As shown in Fig. 1, the image processing system 2 includes an image processing device 1, an image capturing device 110, a first storage device 120, a display device 130, and a second storage device 140. Each of the image capturing device 110, the first storage device 120, the display device 130, and the second storage device 140 is provided so as to be able to communicate with the image processing device 1.

[0043] The imaging device 110 is a device that captures an image of a 3D liver model having a colored vascular network structure. The imaging device 110 is, for example, a confocal microscope. The imaging device 110 uses, for example, a 4x lens, sets the imaging mode to a non-confocal mode that sets a deep focal depth, sets the filter to 647(Ex) / 512(EM), and captures an image of the 3D liver model at a Z-axis position in a range of 0 to 100 μm in 10 μm increments. The imaging device 110 captures an image of a region including the center of the 3D liver model. The imaging device 110 adds up the pixel values ​​and intensities such as color tone of multiple images of each captured 3D liver model at different Z-axis positions of the confocal microscope to obtain a captured image of the 3D liver model. An example of the pixel value is a brightness value. The captured image includes an area in which a colored vascular network structure is depicted.

[0044] The imaging device 110 has a function of outputting a captured image to at least one of the image processing device 1 and the first storage device 120. The first storage device 120 is, for example, a storage medium such as a hard disk. The first storage device 120 stores the captured image described above. The first storage device 120 may also store images captured in the past.

[0045] The image processing device 1 acquires an image of a cell structure and generates an image that allows the shape of the vascular network structure of the cell structure to be grasped. First, the hardware of the image processing device 1 will be described. FIG. 2 is a block diagram showing an example of the hardware configuration of an image processing device according to an embodiment. As shown in FIG. 2, the image processing device 1 is configured as a typical computer system including a CPU (Central Processing Unit) 100, main storage devices such as a ROM (Read Only Memory) 101 and a RAM (Random Access Memory) 102, an input device 103 such as a camera or keyboard, an output device 104 such as a display, and an auxiliary storage device 105 such as a hard disk.

[0046] Each function of the image processing device 1 is realized by loading predetermined computer software onto hardware such as a CPU 100, a ROM 101, and a RAM 102, thereby operating an input device 103 and an output device 104 under the control of the CPU 100, and reading and writing data from and to a main memory device and an auxiliary memory device 105. The image processing device 1 may also include a communication module, etc.

[0047] Returning to FIG. 1, the image processing device 1 includes an acquisition unit 10, a background processing unit 20, a contour extraction unit 30, a skeleton extraction unit 40, a shape detection unit 50, and a display control unit 60.

[0048] The acquiring unit 10 acquires, for example, a captured image of a 3D liver model having a colored vascular network structure from the imaging device 110. The acquiring unit 10 may refer to the first storage device 120 and acquire the captured image from the first storage device 120. The acquiring unit 10 may acquire multiple images from the imaging device 110 at different Z-axis positions of the confocal microscope, and may acquire the captured image by adding up the intensities of pixel values, color tones, etc. of the multiple images instead of the imaging device 110.

[0049] The background processing unit 20 has a cutout function to extract the center of the 3D liver model captured in the captured image, a conversion function to convert into a gradation image that is an image of a predetermined gradation, and a function to remove background signals and noise from the image.

[0050] The background processing unit 20 generates an image by extracting a region of the captured image that includes the center of the 3D liver model as a cutout function. The region that includes the center of the 3D liver model may not include, for example, the periphery of the 3D liver model or the background of the 3D liver model. For example, the background processing unit 20 generates a cutout image that is an image obtained by cutting out a region of the captured image that includes the center of the 3D liver model into a 1 mm square region. The cutout image includes the vascular network structure of the 3D liver model.

[0051] The background processing unit 20 has a conversion function of converting the cutout image into a gradation image of a predetermined gradation. The predetermined gradation is a predetermined gradation, such as 8 gradations, 16 gradations, or 256 gradations. As an example, the background processing unit 20 converts the cutout image into an 8-bit grayscale gradation image. The colored vascular network structure in the cutout image has a high pixel value, so it can be converted to white in the gradation image. The area other than the vascular network structure has a low pixel value in the cutout image, so it can be converted to black in the gradation image.

[0052] The background processor 20 performs a removal function to generate a removed image, which is an image in which the background signal of the grayscale image has been removed. The background processor 20 removes the background signal using, for example, the rolling ball method or the sliding paraboloid method. An example of the radius of the ball or paraboloid is 20 pixels, but this is not limiting. This removes the background signal of the grayscale image and normalizes the pixel values ​​of the vascular network structure.

[0053] The background processor 20 generates an adjusted image, which is an image in which noise from the removed image has been reduced. The background processor 20 reduces noise by adjusting the contrast of the removed image and then subtracting a predetermined pixel value. For example, the background processor 20 stretches the removed image to 256 gradations while maintaining the distribution of pixel values ​​of each pixel. For example, the background processor 20 adjusts the pixel value of the pixel with the smallest pixel value in the removed image to 0 (minimum value) and the pixel value of the pixel with the largest pixel value in the removed image to 255 (maximum value), and then generates an image in which the pixel values ​​of each pixel are distributed according to a distribution similar to the distribution of pixel values ​​of each pixel before adjustment. This function widens the pixel value difference between pixels in the removed image that have high pixel values ​​indicating a vascular network structure and pixels that have low pixel values ​​indicating noise. Next, the background processor 20 subtracts a predetermined pixel value from each pixel value in the image with the new distribution. In the case of a 256-gradation image, for example, 100 is subtracted from the pixel value, but the predetermined pixel value is not limited to 100. In this way, by increasing the difference in pixel value between pixels representing a vascular network structure and pixels representing noise, and then subtracting a predetermined pixel value, it is possible to effectively erase only the noise portion.

[0054] The background processing unit 20 may adjust the distribution of pixel values ​​to make it uniform rather than normalizing it, and subtract a predetermined pixel value from each pixel value in the image having the new distribution. The background processing unit 20 may also reduce noise by applying a filter such as a maximum filter, a minimum filter, or a smoothing filter to the image from which the background signal has been removed.

[0055] The contour extraction unit 30 applies wavelet transform to the adjusted image to generate a contour image in which the contours of the vascular network structure are extracted. The wavelet transform extracts the contours of the subject included in the image. As a result of the wavelet transform, the contour extraction unit 30 decomposes the adjusted image into wavelet coefficients (high-frequency components) and scaling function coefficients (low-frequency components). The contour extraction unit 30 further repeats the above decomposition on the decomposed image, extracts the high-frequency components at each level, and combines them into a single image.

[0056] The contour extraction unit 30 generates a binarized contour image from the combined image by assigning a value of 0 to the intensity of pixels whose pixel values ​​are less than a threshold and a value of 1 to the intensity of pixels whose pixel values ​​are equal to or greater than the threshold. The threshold is determined in advance based on, for example, the properties of the sample and the observation conditions during measurement. The wavelet transform is performed using, for example, a Mexican hat filter. The vascular network structure is represented by pixels with high pixel values ​​in the adjusted image, and is therefore more likely to be extracted as a high-frequency component than pixels of other subjects (elements) with low pixel values. Therefore, in the contour image generated by the contour extraction unit 30, pixels that include the vascular network structure are represented as object pixels with an intensity of 1, and pixels that do not include the vascular network structure are represented as pixels with an intensity of 0.

[0057] The skeleton extraction unit 40 repeatedly removes object pixels from the boundary of the object recognized in the contour image, generating a skeleton image in which a skeleton having a line width of a predetermined number of pixels is extracted. The skeleton extraction unit 40 recognizes an object region, which is a region where multiple object pixels are gathered in the binarized contour image, as a region in which a vascular network structure is depicted, and repeatedly removes object pixels from the boundary of the object region. Removal refers to, for example, setting the intensity of the object pixels to 0.

[0058] The boundary of an object region refers to, for example, an object pixel in the object region adjacent to a pixel above, below, left, or right that does not include a vascular network structure and has an intensity of 0. The skeleton extraction unit 40, for example, sets the predetermined number of pixels to 1 and repeatedly removes the boundaries of the object region until the line width of the object region is represented by one pixel. For example, when focusing on an object pixel (first object pixel) located on the boundary of the object region, if another object pixel (second object pixel) is adjacent to either the above or below the first object pixel, and another object pixel (third object pixel) is adjacent to either the left or right of the first object pixel, and if there are other object pixels (fourth object pixel) adjacent to the second object pixel and the third object pixel, respectively, the first object pixel is removed. The skeleton extraction unit 40 generates a skeleton image in which a skeleton is drawn, which is a collection of object pixels in all object regions drawn in the contour image, each with a line width of one pixel. Note that the predetermined number of pixels is not limited to 1 and can be set to any appropriate value.

[0059] The shape detection unit 50 detects the shape of the vascular network structure based on the skeleton depicted in the skeleton image. The shape of the vascular network structure is, for example, the length of the vascular network structure. The shape detection unit 50 detects the length of the skeleton depicted in the skeleton image as the length of the vascular network structure. The length of the skeleton is, for example, the number of object pixels that make up the skeleton. If the skeleton is branched into multiple branches, the number of pixels of each branched skeleton may be calculated, and the sum of the number of pixels of each branched skeleton may be detected as the length (total length) of the skeleton.

[0060] The shape detection unit 50 generates an extracted image, which is an image in which skeletons drawn in the skeleton image whose lengths are greater than a threshold are extracted. The threshold is determined in advance based on the properties of the sample, the observation conditions during measurement, and the like. The shape detection unit 50 obtains an extracted image by removing skeletons whose lengths are less than the threshold from the skeleton image. This removes small skeletons that are unlikely to constitute a vascular network structure from the skeleton image as noise, and filters out skeletons whose lengths are greater than the threshold. This allows the shape detection unit 50 to generate an extracted image in which skeletons of a vascular network structure are appropriately extracted from the skeleton image. The shape detection unit 50 outputs the lengths of the extracted image, or all of the skeletons in the extracted image, to the display control unit 60. The shape detection unit 50 stores the lengths of the extracted image, or all of the skeletons in the extracted image, in the second storage device 140.

[0061] The shape detection unit 50 may detect the number of branching points of the vascular network structure as the shape of the vascular network structure. For example, the shape detection unit 50 detects the number of branching points by subtracting two from the number of object pixels constituting the skeleton depicted in the skeleton image that are adjacent in at least three directions (upper, lower, left, and right). The shape detection unit 50 may generate an extracted image in which skeletons depicted in the skeleton image with a number of branching points greater than a threshold are extracted. The threshold is determined in advance based on the properties of the sample, the observation conditions during measurement, and the like. Skeletons with a small number of branching points that are unlikely to constitute a vascular network structure are removed as noise from the skeleton image, and skeletons with a number greater than the threshold are extracted by filtering. This allows an extracted image in which the skeleton of the vascular network structure is appropriately extracted from the skeleton image to be generated. The shape detection unit 50 may output the number of branching points of the extracted image or all skeletons in the extracted image to the display control unit 60. The shape detection unit 50 may store the number of branching points of the extracted image or all skeletons in the extracted image in the second storage device 140. The shape detection unit 50 may output the number of skeletal branching points within a specific area in the extracted image, or the number of skeletal branching points per unit area in the extracted image to the display control unit 60, or may store it in the second storage device 140.

[0062] The display control unit 60 is connected to the acquisition unit 10, background processing unit 20, contour extraction unit 30, skeleton extraction unit 40 or shape detection unit 50 within the image processing device 1, and a display device 130 external to the image processing device 1. The display control unit 60 controls the display on the display device 130. The display control unit 60 controls the display device 130 to display a cutout image, a gradation image, a removed image, an adjusted image, a contour image, a skeleton image or an extracted image. The display control unit 60 may cause the display device 130 to display a list of all skeleton lengths or a list of the number of branch points in the extracted image.

[0063] The display device 130 is a device that is connected to the display control unit 60 of the image processing device 1 and displays the content controlled by the display control unit 60. The display device 130 displays a cutout image, a gradation image, a subtracted image, an adjusted image, a contour image, a skeleton image, or an extracted image. The display device 130 may display a list of all skeleton lengths or a list of the number of branch points in the extracted image. The display device 130 is, for example, a display.

[0064] The second storage device 140 is a storage medium such as a hard disk. The second storage device 140 stores data used in the image processing device 1, such as cut-out images, gradation images, subtracted images, adjusted images, contour images, skeleton images, extracted images, a list of all skeleton lengths in the extracted images, or a list of the number of branch points. The first storage device 120 and the second storage device 140 may be the same storage device.

[0065] [Image processing method] Next, the operation of the image processing system 2 will be described. First, the process of acquiring a captured image to be processed by the image processing device 1 will be described. Fig. 3 is a flowchart showing an example of the process of acquiring an image to be processed by the image processing method according to the embodiment. The method shown in Fig. 3 is started, for example, when cell lines, reagents, consumables, etc. related to the creation of a 3D liver model are prepared.

[0066] First, in the coloring process (S11), the vascular network structure of the 3D liver model is colored by an operator, etc. Here, the above-mentioned production process, medication process, and coloring control process are executed.

[0067] Next, in the imaging process (S13), the imaging device 110 of the image processing system 2 images the 3D liver model having the vascular network structure colored by the coloring process (S11). The imaging device 110 images an area including the center of the 3D liver model. The imaging device 110 adds up the intensities of pixel values ​​and color tones of multiple images of each imaged 3D liver model at different Z-axis positions of the confocal microscope, to acquire an image of the 3D liver model. The imaging device 110 outputs the captured image to the acquisition unit 10 and the second storage device 140.

[0068] This completes the flowchart shown in Fig. 3. Execution of the flowchart shown in Fig. 3 results in a captured image that is the subject of the image processing method according to the embodiment. Next, the operation of the image processing device 1 of the image processing system 2 will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the image processing method according to the embodiment. The flowchart shown in Fig. 4 is started, for example, in response to a start operation by an operator, after the flowchart shown in Fig. 3 has finished.

[0069] First, in the image acquisition process (S15), the acquisition unit 10 of the image processing device 1 acquires from the imaging device 110 or the first storage device 120 a captured image of a 3D liver model having a colored vascular network structure.

[0070] Next, in the gradation conversion process (S17), the background processing unit 20 of the image processing device 1 generates a clipped image by extracting an area including the center of the 3D liver model from the captured image, and converts it into a gradation image. The background processing unit 20 generates, for example, multiple clipped images from the captured image, and converts each of them into a gradation image.

[0071] Next, in the noise removal process (S19), the background processing unit 20 generates a removed image by removing background signals from the gradation image generated in the gradation conversion process (S17). The background processing unit 20 removes background signals by, for example, the sliding paravoid method.

[0072] Next, in the pixel value adjustment process (S21), the background processing unit 20 generates an adjusted image by reducing the noise of the removed image generated in the noise removal process (S19). The background processing unit 20 increases the difference in pixel value between each pixel while maintaining the distribution of pixel values ​​of each pixel in the removed image, and subtracts a predetermined pixel value to generate the adjusted image.

[0073] Next, in the contour image generation process (S23), the contour extraction unit 30 of the image processing device 1 applies wavelet transform to the adjusted image generated by the pixel value adjustment process (S21) to generate a contour image in which the contours of the vascular network structure are extracted. The contour extraction unit 30 repeats decomposition using wavelet transform, extracts high-frequency components at each level, and combines them into a single image. The contour extraction unit 30 generates a binarized contour image in which the intensity of pixels whose pixel values ​​are less than a threshold is set to 0, and the intensity of pixels whose pixel values ​​are equal to or greater than the threshold is set to 1.

[0074] Next, in the skeleton image generation process (S25), the skeleton extraction unit 40 of the image processing device 1 repeatedly removes pixels from the boundary of the object drawn in the outline image to generate a skeleton image in which a skeleton having a line width of a predetermined number of pixels is extracted. The skeleton extraction unit 40 repeatedly removes object pixels from the boundary of the object region in the binarized outline image until the width of the object region reaches the predetermined number of pixels.

[0075] Next, in the length detection process (S27), the shape detection unit 50 of the image processing device 1 detects the length of the vascular network structure based on the skeleton drawn in the skeleton image. The shape detection unit 50 calculates, for example, the number of pixels of each skeleton in the skeleton image.

[0076] Next, in the extracted image generation process (S29), the shape detection unit 50 generates an extracted image in which skeletons drawn in the skeleton image that have lengths greater than a threshold value are extracted.

[0077] Next, in the output process (S31), the display control unit 60 of the image processing device 1 displays a list of lengths of the extracted image or all skeletons in the extracted image on the display device 130 of the image processing system 2. The shape detection unit 50 stores the lengths of the extracted image or all skeletons in the extracted image in the second storage device 140 of the image processing system 2.

[0078] This completes the process of the flowchart shown in Fig. 4. By executing the flowchart shown in Fig. 4, information about the length of the vascular network structure is automatically output from the captured image.

[0079] [Image processing program] An image processing program for causing a computer to function as the image processing device 1 will be described. The image processing program includes a main module, an acquisition module, a contour generation module, and a skeleton generation module. The image processing program may also include a background processing module and a shape detection module. The main module is a part that performs overall control of the device. The functions realized by executing the acquisition module, contour generation module, skeleton generation module, background processing module, and shape detection module are the same as the functions of the acquisition unit 10, background processing unit 20, contour extraction unit 30, skeleton extraction unit 40, shape detection unit 50, and display control unit 60 of the image processing device 1 described above, respectively.

[0080] [Summary of the embodiment] According to the image processing method and image processing program of this embodiment, an image of a cellular structure having a vascular network structure is processed. First, a wavelet transform is applied to the image. As a result, a contour image in which the contour of the vascular network structure is extracted is generated (contour image generation process (S23)). Next, object pixels are repeatedly removed from the boundary of the object recognized in the contour image. As a result, a skeleton image in which the skeleton of the vascular network structure is extracted is generated (skeleton image generation process (S25)). By generating the skeleton image from the contour image, a skeleton that more accurately reflects the shape of the blood vessels can be obtained compared to when the skeleton image is generated directly from the captured image. Therefore, this image processing method and image processing program can generate an image that allows the shape of the vascular network structure to be more accurately grasped.

[0081] In addition, since the image processing method according to this embodiment includes a step of detecting the shape of the vascular network structure based on the skeletal image (length detection process (S27) or extracted image generation process (S29)), the characteristics of the vascular network structure can be evaluated using the detected shape of the vascular network structure.

[0082] In addition, since the image processing method according to this embodiment includes a step of detecting the shape of the vascular network structure (length detection process (S27) or extracted image generation process (S29)), the characteristics of the vascular network structure can be evaluated using the length of the detected vascular network structure.

[0083] In addition, the image processing method of this embodiment targets captured images in which the area including the center of the 3D liver model is captured, thereby making it possible to appropriately extract areas in which the vascular network structure of the 3D liver model is likely to be captured.

[0084] Furthermore, the image processing method according to this embodiment is executed after the acquisition step (image acquisition process (S15)) and before the step of generating a contour image (contour image generation process (S23)), and includes a step of generating an image in which the background signal of the vascular network structure has been removed from the image (noise removal process (S19)). As a result, the background signal of the image is removed, and the skeleton representing the vascular network structure can be extracted more clearly.

[0085] Furthermore, the image processing method according to this embodiment is executed after the step of generating a skeleton image (skeleton image generation process (S25)), and includes the steps of generating an extracted image in which skeletons drawn in the skeleton image whose length is greater than a threshold are extracted (length detection process (S27) and extracted image generation process (S29)), so that skeletons that are unlikely to form a vascular network structure can be removed as noise.

[0086] Furthermore, the image processing method according to this embodiment is executed after the acquisition step (image acquisition process (S15)) and before the step of generating a contour image (contour image generation process (S23)), and includes a step of generating an image by converting the image into a gradation image of a predetermined gradation (gradation conversion process (S17)). Therefore, the captured image is converted into a gradation image, and processing of the converted image can be easily carried out.

[0087] In addition, the image processing method of this embodiment further includes a step (coloring process (S11)) that is executed before the acquisition step (image acquisition process (S15)) of immunostaining cells having a vascular network structure, fluorescently modifying the cells having a vascular network structure in advance, or coloring the vascular network structure by a combination of immunostaining and fluorescent modification, and a step (imaging process (S13)) of imaging the 3D liver model having a vascular network structure that has been colored by the coloring step.By doing this, the coloring process (S11) and the imaging process (S13) can be executed on the 3D liver model having a vascular network structure, and the shape of the vascular network structure can be accurately grasped for each 3D liver model.

[0088] Furthermore, the image processing method according to this embodiment targets a 3D liver model having vascular endothelial cells, thereby extracting the vascular network structure in the vascular endothelial cells and detecting the shape of the vascular network structure.

[0089] Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above embodiments. For example, the image processing device 1 may include the imaging device 110, the first storage device 120, the display device 130, or the second storage device 140 in the image processing system 2. The image processing device 1 may not include the background processing unit 20, the shape detection unit 50, and the display control unit 60. [Example]

[0090] The effects of the image processing method and the image processing program will be described with reference to Figures 5 to 10. In Figures 5 to 8, images were taken of a control sample in which cyclophosphamide was not administered to a 3D liver model. Hereinafter, unless otherwise specified, it is assumed that the respective processes in the flowcharts shown in Figures 3 and 4 are performed on the captured images.

[0091] [Effect of contour image generation processing] In the flowchart shown in Fig. 4, the resulting skeletons were compared between when the contour image generation process (S23) was performed and when the contour image generation process (S23) was not performed (or when other processes were performed). Fig. 5 (a) to (f) show examples of images obtained by the contour image generation process and the skeleton image generation process. Hereinafter, the image immediately before the skeleton image generation process (S25) is referred to as the original image.

[0092] (Comparative Example 1) Fig. 5(a) is an image in which the contour image generation process (S23) was not performed. Fig. 5(b) is an image (Comparative Example 1) in which the skeleton image generation process (S25) was performed using the image shown in Fig. 5(a) as the original image. As shown in Fig. 5(b), a straight line shape was displayed regardless of the vascular network structure of the original image, and the skeleton of the vascular network structure was not properly extracted as a skeleton image.

[0093] (Comparative Example 2) Figure 5(c) shows an image obtained by performing binarization on the image shown in Figure 5(a) instead of the contour image generation process (S23). Instead, a threshold value for pixel values ​​is set, and only pixels equal to or greater than the threshold are displayed in white, while pixels less than the threshold are displayed in black. Figure 5(d) shows an image (Comparative Example 2) obtained by performing the skeleton image generation process (S25) on the image shown in Figure 5(c) as the original image. As shown in Figure 5(d), a skeleton that matches the original image to a certain extent is extracted. However, for example, in areas showing small cavities in the vascular network structure of the original image, the skeleton is not accurately extracted, resulting in a skeleton pattern that is significantly more complex than the corresponding area in the original image. This is thought to be due to the presence of hollow areas in the pattern generated by the binarization process. The skeleton image generation process erodes objects recognized in the image from the edge side until they are one pixel wide. However, the original image shown in Figure 5(c) contains small black dots (holes) throughout the overall pattern extracted in white. In the skeleton image generation process, this hole is recognized as part of the edge, which is thought to hinder accurate skeleton extraction.

[0094] Example 1 FIG. 5(e) is an image obtained by executing the contour image generation process (S23) on the image shown in FIG. 5(a). FIG. 5(f) is an image (Example 1) obtained by executing the skeleton image generation process (S25) on the image shown in FIG. 5(e) as the original image. As shown in FIG. 5(e), information such as the thickness of the blood vessels is lost from the image shown in FIG. 5(a). However, as shown in FIG. 5(f), compared with Comparative Example 1 and Comparative Example 2, a skeleton consistent with the original image was extracted. In particular, a skeleton accurately representing small cavities in the vascular network structure of the original image was extracted. Thus, it was confirmed that, in extracting a skeleton in the skeleton image generation process, it is not necessary to accurately capture the vascular region of the original image; it is sufficient to extract an approximate outline.

[0095] As described above, Example 1, Comparative Example 1, and Comparative Example 2 confirmed that by performing the contour image generation process (S23), the skeleton of the vascular network structure in the image can be more appropriately extracted in the skeleton image generation process (S25). The small black dots that were problematic in Comparative Example 2 are difficult to distinguish from the mesh-like areas in the original image by definition, and it is difficult to mechanically fill them in using an algorithm. While it is conceivable to perform binarization processing by setting a threshold value to prevent the formation of the small black dots in Comparative Example 2, this would result in increased noise and would be unrealistic. Example 1 and Comparative Example 2 confirmed that the problems caused by the small black dots described above can be solved by a new approach in which the skeleton is extracted after the rough contour of the original image is extracted.

[0096] [Effects of pixel value adjustment processing] The resulting skeletons were compared when the pixel value adjustment process (S21) was performed and when the pixel value adjustment process (S21) was not performed. Fig. 6(a) to (d) show examples of images obtained by the gradation conversion process, noise removal process, contour image generation process, and skeleton image generation process. Fig. 7(a) to (d) show examples of images obtained by the pixel value adjustment process, contour image generation process, and skeleton image generation process.

[0097] (Comparative Example 3) FIG. 6(a) is a grayscale image generated by the grayscale conversion process (S17). FIG. 6(b) is a noise-removed image obtained by performing the noise removal process (S19) on the grayscale image shown in FIG. 6(a). FIG. 6(c) is an image obtained by performing the contour image generation process (S23) on the image shown in FIG. 6(b) without performing the pixel value adjustment process (S21). FIG. 6(d) is an image (Comparative Example 3) obtained by performing the skeleton image generation process (S25) on the image shown in FIG. 6(c). As shown in FIG. 6(d), when the pixel value adjustment process (S21) was not performed, a vascular network structure connected to other vascular networks was displayed even in areas in the grayscale image where the vascular network structure was not clearly visible, and the contour and skeleton of the vascular network structure were not properly extracted as a contour image. This is thought to be due to the contour image generation process (S23) failing to distinguish between actual vascular regions and noise and extracting them as contours.

[0098] Example 2 FIG. 7(a) is an image obtained by adjusting the distribution of pixel values ​​for the same removed image as that shown in FIG. 6(b). FIG. 7(b) is an image obtained by subtracting a predetermined pixel value from the pixel value of each pixel of the image shown in FIG. 7(a). That is, FIG. 7(a) is an intermediate image in the pixel value adjustment process (S21), and FIG. 7(b) is an adjusted image obtained by completing the pixel value adjustment process (S21). FIG. 7(c) is a contour image obtained by performing a contour image generation process (S23) on the image shown in FIG. 7(b). FIG. 7(d) is an image (Example 2) obtained by performing a skeleton image generation process (S25) on the contour image shown in FIG. 7(c).

[0099] As shown in (a) and (b) of Figure 7, it was confirmed that noise was removed by performing the pixel value adjustment process (S21). Furthermore, as shown in (d) of Figure 7, compared to Comparative Example 3 shown in (d) of Figure 6, the appearance of a vascular network structure connected to other vascular networks in an area where the vascular network structure is not clearly visible in the grayscale image was suppressed, and the contour and skeleton of the vascular network structure were appropriately extracted as a contour image. From the above, it was found that by performing the pixel value adjustment process (S21), the contour of the vascular network structure in the image was more appropriately extracted in the contour image generation process (S23), and the skeleton of the vascular network structure in the image was more appropriately extracted in the skeleton image generation process (S25).

[0100] [Effect of extracted image generation process] Next, the resulting skeletons were compared between when the extraction image generation process (S29) was performed and when the extraction image generation process (S29) was not performed. Fig. 8(a) is a captured image. Fig. 8(b) is a skeleton image (Comparative Example 4) before the extraction image generation process (S29) was performed. Fig. 8(c) is an extraction image (Example 3) after the extraction image generation process (S29) was performed.

[0101] By comparing (b) and (c) of Figure 8, when the extraction image generation process (S29) was performed, skeletons with short skeleton lengths were removed, so that the extraction image displayed a structure similar to the vascular network structure visible in the captured image compared to the skeleton image, and the skeleton of the vascular network structure was properly extracted. From the above, it was found that by performing the extraction image generation process (S29), the skeleton of the vascular network structure in the image could be more properly extracted.

[0102] [Quantitative evaluation of vascular networks] This section describes quantitative evaluation of the vascular network of a sample group in which 3D liver models (cell structures) prepared by the preparation process described in the embodiment were exposed to cyclophosphamide at various concentrations. (a) to (f) in Figure 9 are images acquired according to the flowchart shown in Figure 3, with cyclophosphamide concentrations of 0 μM, 143 μM, 430 μM, 1430 μM, 4300 μM, and 14300 μM, respectively. (g) to (l) in Figure 9 are extracted images obtained by performing the processes in the flowchart shown in Figure 4 on the images shown in (a) to (f) in Figure 9.

[0103] As can be seen from the captured images shown in (a) to (f) of Figure 9, the higher the cyclophosphamide concentration, the shorter the vascular network structure tends to be. The extracted images shown in (g) to (l) of Figure 9 also reflect the tendency for the skeleton length to shorten as the cyclophosphamide concentration increases.

[0104] Figures 10(a) and 10(b) show the results of extracting the skeleton length and the number of branching points in the extracted images shown in Figures 9(g) to 9(l). Figure 10(a) shows the correspondence between the skeleton length and the cyclophosphamide dosage concentration in the extracted image according to the embodiment, and Figure 10(b) shows the correspondence between the number of skeleton branching points in the extracted image and the cyclophosphamide dosage concentration. As shown in Figures 10(a) and 10(b), the shape of the vascular network structure changes significantly and the number of vascular network structures decreases when the cyclophosphamide dosage concentration exceeds a certain level. Therefore, it was confirmed that the extracted images generated by the image processing method can appropriately grasp the characteristics and trends of the shape of the vascular network structure. [Explanation of symbols]

[0105] 1...image processing device, 2...image processing system, 10...acquisition unit, 20...background processing unit, 30...contour extraction unit, 40...skeleton extraction unit, 50...shape detection unit, 60...display control unit, 110...imaging device, 120...first storage device, 130...display device, 140...second storage device.

Claims

1. acquiring an image of a cell structure having a colored vascular network structure; applying a wavelet transform to the image to generate a contour image in which the contour of the vascular network structure is extracted; a step of repeatedly excluding pixels of the object from the boundary of the object recognized in the contour image, and generating a skeleton image in which a skeleton having a line width of a predetermined number of pixels is extracted; a step of generating an extracted image, which is executed after the step of generating the skeleton image, and in which skeletons drawn in the skeleton image whose lengths are greater than a threshold are extracted; An image processing method comprising:

2. The image processing method according to claim 1 , further comprising the step of detecting a shape of the vascular network structure based on the skeleton image.

3. The image processing method according to claim 2 , wherein the shape of the vascular network structure is the length of the vascular network structure.

4. 4. The image processing method according to claim 1, wherein the image is an image of a region including a center of the cell structure.

5. The image processing method according to any one of claims 1 to 4, further comprising a step of generating an image in which background signals of the image have been removed, the step being executed after the acquiring step and before the step of generating the contour image.

6. 6. The image processing method according to claim 1, further comprising a step of converting the image into an image of a predetermined tone, the step being executed after the acquiring step and before the step of generating the contour image.

7. a step, which is performed before the obtaining step, of immunostaining the cells having the vascular network structure, fluorescently modifying the cells having the vascular network structure in advance, or a combination of immunostaining and fluorescent modification to color the vascular network structure; a step of imaging the cell structure having the vascular network structure colored by the coloring step; further comprising 7. The image processing method according to claim 1, wherein the acquiring step acquires an image captured by the imaging step.

8. The image processing method according to any one of claims 1 to 7, wherein the cell structure comprises a vascular endothelial cell.

Images