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 constituting the vascular network structure of this cell structure are immunostained with an anti-CD31 antibody. This makes it possible to observe the vascular network structure using 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, it is dense, making it difficult to quantitatively analyze the vascular network structure. As an example of a method for quantitatively analyzing the vascular network structure, a method of counting the number of cells in the cell structure by subjecting the fabricated cell structure to a trypsin treatment or the like is considered. However, this method makes it difficult to count only the cells that constitute the vascular network structure. Alternatively, it is also possible to distinguish and evaluate the vascular network structure based on the intensity of fluorescence caused by the cells that constitute the vascular network structure in an image of the cell structure after immunostaining. However, since the cells to be stained are not necessarily limited to the cells that constitute the vascular network structure, this is insufficient for evaluating the shape, such as the length, of the vascular network structure. The present disclosure provides a method and a program for generating an image that allows the shape of the vascular network structure to be more accurately grasped. [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, pixels of the object 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 is 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 the vascular network structure based on the skeleton image, in which case the image processing method can evaluate the 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 use the detected length of the vascular network structure to evaluate the characteristics 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 this case, the image processing method can appropriately extract a region that is likely to include 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 with a background signal of the image removed, the step being performed after the acquiring step and before the step of generating a contour image, in which the background signal of the image is removed, so that the image processing method can more clearly extract the skeleton showing the 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 are extracted that have a length greater than a threshold value. In this case, since blood vessel structures whose skeleton lengths are equal to or less than the threshold value are not extracted, 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 tone, the step being performed after the acquiring step and before the step of generating the contour image. In this case, since the captured image is converted into an image of a predetermined tone, the image processing method can easily proceed with processing the converted image.

[0013] In one embodiment, the image processing method is performed before the acquiring step, and further includes a step 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 color the vascular network structure, and a step of imaging the cell structure having the vascular network structure colored by the coloring step, and the acquiring step may obtain an image captured by the imaging step. In this case, the image processing method performs the coloring step and the imaging step on the cell structure having a vascular network structure, thereby making it possible to more accurately grasp the shape of the vascular network structure for each cell structure.

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

[0015] An image processing program relating to another aspect of the present disclosure causes a computer to execute 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 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 effects as the above-mentioned image processing method. Effect 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 description 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 device according to an embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the image processing device. [Diagram 3] FIG. 3 is a flowchart showing an example of an image acquisition process that is a 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. [Diagram 5] FIG. 5 is a diagram showing an example of an image acquired by the contour image generating process and the skeleton image generating 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 acquired 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 generating 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] FIG. 10(a) is a diagram showing the correspondence between the skeletal length in an extracted image of the embodiment and the dosage concentration of cyclophosphamide, and FIG. 10(b) is a diagram showing the correspondence between the number of branching points of the skeletal structure in an extracted image and the dosage concentration of cyclophosphamide. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0020] [Outline of cell structure] First, a cell structure to be imaged by the image processing device according to the embodiment will be outlined. 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 is capable of evaluating 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, and vascular endothelial cells as interstitial cells. The cell structure may further include an extracellular matrix component that functions as a scaffolding material for cells, and may include a component (e.g., a polyelectrolyte) that supports the formation of other cell structures.

[0021] In the present specification, the term "extracellular matrix component" refers to an assembly of extracellular matrix molecules formed by a plurality of extracellular matrix molecules. The extracellular matrix refers to a substance present outside the cells in an organism. Any substance can be used as the extracellular matrix as long as it does not adversely affect the growth of cells and the formation of cell aggregates. 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, and the formation of a three-dimensional cell structure is further promoted. In this embodiment, the extracellular matrix component is preferably a substance present outside the 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, so long as it does not adversely affect cell growth and cell aggregate formation.

[0022] The extracellular matrix component may have a repeat of a sequence represented by Gly-XY, which is characteristic of collagen. Here, Gly represents a glycine residue, and X and Y each independently represent any amino acid residue. The multiple Gly-XYs may be the same or different. By having a repeat of a sequence represented by Gly-XY, there is less restriction on the arrangement of the molecular chain, and the function as a scaffold material is further improved. In the extracellular matrix component having a repeat of a sequence represented by Gly-XY, the ratio of the sequence represented by Gly-XY to the total amino acid sequence may be 80% or more, preferably 95% or more. In addition, the extracellular matrix component may have an RGD sequence. The RGD sequence refers to a sequence represented by Arg-Gly-Asp (arginine residue-glycine residue-aspartic acid residue). When the extracellular matrix component has an RGD sequence, cell adhesion is further promoted, and the scaffold material is further suitable. Extracellular matrix components containing a sequence represented by Gly-XY and an RGD sequence include collagen, fibronectin, vitronectin, laminin, cadherin, and the like.

[0023] The shape of the extracellular matrix component may be, for example, fibrous. The term "fibrous" refers to a shape composed of thread-like extracellular matrix components, or a shape composed of thread-like extracellular matrix components cross-linked between molecules. At least a part of the extracellular matrix component may be fibrous. The shape of the extracellular matrix component is the shape of a mass of extracellular matrix components (aggregates 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. The fibrous extracellular matrix component includes thin threads (fibers) formed by the aggregation of multiple thread-like extracellular matrix molecules, threads formed by further aggregation of filaments, and those obtained by defibrating these threads. When the extracellular matrix component has a fibrous shape, the RGD sequence is preserved without being destroyed in the fibrous extracellular matrix component, and the fibrous extracellular matrix component can function more effectively as a scaffold for cell adhesion.

[0024] The polyelectrolyte, which is a component that supports the formation of cell structures, is a polymer compound having electrolyte 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 be one of the above-mentioned types, or may be a combination of two or more types.

[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, a cell structure with excellent responsiveness to hepatotoxic substances can be more easily obtained. When the cell structure contains heparin, the effect becomes even more remarkable.

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

[0027] More specifically, the 3D liver model (cell structure) in which the vascular network, which is the subject of imaging by the image processing device, is colored is obtained by executing a production process for producing the 3D liver model, a drug administration process for administering a drug to the 3D liver model, and a coloring control process for coloring the 3D liver model. When the degree of damage to the vascular network structure is not observed, that is, when a compound is not administered to the 3D liver model, the drug administration process does not need to be executed.

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

[0029] [Table 1]

[0030] Table 2 shows an example of reagents and consumables used in the image processing system. Note that general-purpose research materials such as disposable pipettes are omitted in Table 2. In the first collection process, for example, the operator prepares purchased human hepatic stellate cells (Lx2) and human hepatic sinusoidal endothelial cells (SEC) and measures the amount of each cell. Note that in the first collection 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 subculture based on the manufacturer's recommended protocol, and recover them from the culture flask and petri dish with trypsin based on a normal method, and then measure the amount of each cell. In the second collection process, for example, the operator prepares purchased PXB cells and measures the amount of 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 intracellular 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 interstitial cells.

[0033] In the suspension acquisition process, for example, a heparin-collagen solution is prepared by mixing equal amounts 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 above-mentioned prepared heparin-collagen solution is added to each cell mixture obtained in the mixture acquisition process, and the cells are suspended until they are no longer visible, and then centrifuged (400 g x 2 min) to obtain a viscous body. After the supernatant is removed from the viscous body solution, a solvent is added so that the final volume of the viscous body solution becomes the "number of wells to be seeded" x 2 μL of solution, and a cell suspension is obtained.

[0034] In the gel formation process, for example, the cell suspension obtained in the suspension acquisition process, a 10 mg / mL fibrinogen solution, and a 20 U / mL thrombin solution (solvent: HCM) are mixed 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, after a droplet of the 10 mg / mL fibrinogen solution is formed, the cell suspension obtained in the suspension acquisition process is added to the inside of the droplet. Also, for example, after 2 μL of the cell suspension obtained in the suspension acquisition process is seeded to form a droplet, 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 in which a cell suspension and a 10 mg / mL fibrinogen solution are mixed. The order in which the cell suspension, the 10 mg / mL fibrinogen solution, and the 20 U / mL thrombin solution (solvent: HCM) are mixed is not limited to the above, and may be any order. The droplet in which the cell suspension, the 10 mg / mL fibrinogen solution, and the 20 U / mL thrombin solution (solvent: HCM) are mixed 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 lump 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 is formed in the gel formation process, and a 3D model is obtained. The above processes complete the production process. Note that in the mixture acquisition process, suspension acquisition process, gel formation process, and model acquisition process in the production process, the values ​​of the total cell amount, intra-tissue cell ratio, reagent concentration, reagent solution volume, centrifugation time, standing time, etc. are not limited to the above values ​​and may be changed appropriately depending on the cell structure to be produced.

[0036] Next, the drug administration process is performed. In the drug administration process, the 3D liver model created in the creation process is administered with each medium containing monoclotaline at concentrations of 2000 μM, 666 μM, 222 μM, and 74 μM on the first and fourth days. Each compound is dissolved in DMSO (Dimethyl sulfoxide) at a high concentration in advance and stored, and DMSO is contained in the medium at a concentration of 1% at the time of administration. In addition, since 1% DMSO is contained in each drug administration condition, medium exchange with a medium containing only 1% DMSO is also performed at the same time as a negative control. This completes the drug administration step.

[0037] Next, a color development control process is executed. As an example, the color development control process includes 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, in the primary antibody treatment, mouse anti-CD31 antibody is diluted 100-fold with BSA solution to obtain a primary antibody solution. 100 μL of the primary antibody solution is added to the insert of each well and allowed to stand at 4° C. for 24 hours. After standing, the primary antibody solution is washed away.

[0041] Next, in the secondary antibody treatment, the secondary antibody is diluted 200 times with the BSA solution to obtain a secondary antibody solution. 100 μL of the secondary antibody solution is added to the insert of each well, and the solution is left standing for 1 hour at room temperature in the dark. After standing, the secondary antibody solution is washed away, and 100 μL of PBS is added to each well. Through the above treatment, the color development control treatment is completed, and a 3D liver model in which the vascular network structure is colored can be obtained. The color development control treatment may be performed, for example, using a known method.

[0042] [Image processing system] Fig. 1 is a block diagram showing an example of functions of an image processing system including an image processing device according to an embodiment. As shown in Fig. 1, an 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 images 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 a non-confocal mode in which the focal depth is set thick as the imaging mode, sets a filter to 647 (Ex) / 512 (EM), and images the 3D liver model at a Z-axis position in the range of 0 to 100 μm at intervals of 10 μm. The imaging device 110 images an area including the center of the 3D liver model. The imaging device 110 adds up the pixel values ​​and intensities such as color tone of a plurality of images having different Z-axis positions of the confocal microscope for each imaged 3D liver model, and obtains an image that is an image of the 3D liver model. An example of the pixel value is a luminance value. The captured image includes an area in which a colored vascular network structure is drawn.

[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 understood. First, the hardware of the image processing device 1 will be described. FIG. 2 is a block diagram showing an example of a 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 normal 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 a 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, 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 storage device and an auxiliary storage device 105. The image processing device 1 may also include a communication module.

[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 having different Z-axis positions of the confocal microscope from the imaging device 110, and may acquire the captured image by adding up the intensities of pixel values, color tones, and the like of the multiple images instead of the imaging device 110.

[0049] The background processing unit 20 has a cutout function for extracting the central portion of the 3D liver model captured in the captured image, a conversion function for converting the image into a gradation image that is an image of a predetermined gradation, and a function for removing background signals and noise from the image.

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

[0051] The background processing unit 20 converts the cutout image into a gradation image of a predetermined gradation as a conversion function. The predetermined gradation is a gradation determined in advance, for example, 8 gradations, 16 gradations, 256 gradations, etc. As an example, the background processing unit 20 converts the cutout image into an 8-bit grayscale gradation image. The vascular network structure that is colored 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 generates a removed image, which is an image obtained by removing background signals from a grayscale image, as a removal function. The background processor 20 removes background signals by, for example, a rolling ball method or a sliding paraboloid method. An example of the radius of the ball or paraboloid is 20 pixels, but is not limited to this. This removes the background signals from the grayscale image, and normalizes the pixel values ​​of the vascular network structure.

[0053] The background processing unit 20 generates an adjusted image in which the noise of the removed image has been reduced. The background processing unit 20 reduces noise by adjusting the contrast of the removed image and subtracting a predetermined pixel value. For example, the background processing unit 20 stretches the removed image to 256 gradations while maintaining the distribution of pixel values ​​of each pixel. As an example, the background processing unit 20 adjusts the pixel value of the pixel having the smallest pixel value in the removed image to 0 (minimum value), adjusts the pixel value of the pixel having the largest pixel value in the removed image to 255 (maximum value), and generates an image in which the pixel values ​​of each pixel are distributed according to a distribution similar to the distribution of the pixel values ​​of each pixel before adjustment. This function makes it possible to widen the pixel value difference between pixels in the removed image that show a vascular network structure with high pixel values ​​and pixels that show noise with low pixel values. Next, the background processing unit 20 subtracts a predetermined pixel value from each pixel value in the image having the new distribution. In the case of an image with 256 gradations, 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 processor 20 may adjust the distribution of pixel values ​​to be uniform rather than normalizing it, and subtract a predetermined pixel value from each pixel value in the image having the new distribution. The background processor 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 transformation to the adjusted image to generate a contour image in which the contours of the vascular network structure are extracted. The wavelet transformation extracts the contours of the subject contained in the image. As the wavelet transformation, 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 of each level, and combines them into a single image.

[0056] The contour extraction unit 30 generates a contour image by binarizing the combined image by setting the intensity of pixels whose pixel values ​​are less than a threshold to 0 and the intensity of pixels whose pixel values ​​are equal to or greater than the threshold to 1. The threshold is determined in advance based on, for example, the properties of the sample and the observation conditions during measurement. The wavelet transformation is performed by, for example, a Mexican hat filter. Since the vascular network structure is represented by pixels with high pixel values ​​in the adjusted image, it is 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 in which the vascular network structure is reflected are represented as object pixels with an intensity of 1, and pixels in which the vascular network structure is not reflected 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, and generates 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 in which a plurality of object pixels are gathered in the binarized contour image, as a region in which a vascular network structure is depicted, and repeatedly removes the 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 in which no vascular network structure is reflected and the intensity is 0 on either the upper side, lower side, left side, or right side. 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 expressed by one pixel. For example, when focusing on a certain 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 upper side or lower side of the first object pixel, and another object pixel (third object pixel) is adjacent to either the left side or right side of the first object pixel, and another object pixel (fourth object pixel) is 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 set of object pixels in which the line width of all object regions drawn in the contour image is one pixel. Note that the predetermined number of pixels is not limited to 1, and an appropriate value can be set.

[0059] The shape detection unit 50 detects the shape of the vascular network structure based on the skeleton drawn 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 drawn in the skeleton image as the length of the vascular network structure. The length of the skeleton is, for example, the number of pixels of the object pixels constituting the skeleton. When the skeleton is branched into multiple parts, 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 in which skeletons drawn in the skeleton image are extracted that have a length greater than a threshold value. The threshold value 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 that are less than the threshold value from the skeleton image. As a result, small skeletons that are difficult to be considered to constitute a vascular network structure are removed as noise from the skeleton image, and skeletons that are greater than the threshold value are filtered out. Therefore, the shape detection unit 50 can 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 length 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 length 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 branch 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 branch points obtained by subtracting 2 from the number of object pixels constituting the skeleton drawn in the skeleton image that are adjacent in at least three directions, namely, the upper side, the lower side, the left side, and the right side. The shape detection unit 50 may generate an extracted image in which skeletons drawn in the skeleton image with a number of branch 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. Among the skeleton images, skeletons with a small number of branch points that are difficult to regard as constituting the vascular network structure are removed as noise, and a skeleton with a number greater than the threshold is extracted by screening. This makes it possible to generate an extracted image in which the skeleton of the vascular network structure is appropriately extracted from the skeleton image. The shape detection unit 50 may output the extracted image or the number of branch points of all skeletons in the extracted image to the display control unit 60. The shape detection unit 50 may store the extracted image or the number of branch points of 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 memory 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 in the image processing device 1, and a display device 130 outside 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 cut-out 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 contents controlled by the display control unit 60. The display device 130 displays a cut-out image, a gradation image, a removed 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 a cut-out image, a gradation image, a removed image, an adjusted image, a contour image, a skeleton image, an extracted image, a list of all skeleton lengths in the extracted image, 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 a cell line, a reagent, 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 different Z-axis positions of the confocal microscope for each imaged 3D liver model, to obtain an image of the 3D liver model. The imaging device 110 outputs the image to the acquisition unit 10 and the second storage device 140.

[0068] This is the end of the flowchart shown in Fig. 3. By executing the flowchart shown in Fig. 3, a captured image that is the subject of the image processing method according to the embodiment is obtained. 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 ended.

[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 cutout 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 a plurality of cutout images from the captured image, for example, and converts each of them into a gradation image.

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

[0072] Next, in the pixel value adjustment process (S21), the background processor 20 generates an adjusted image by reducing noise in the removed image generated in the noise removal process (S19). The background processor 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 an 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 contour of the vascular network structure is extracted. The contour extraction unit 30 repeats the decomposition by wavelet transform, extracts high-frequency components at each level, and combines them into one image. The contour extraction unit 30 generates a binarized contour image by setting the intensity of pixels whose pixel values ​​are less than a threshold to 0 and the intensity of pixels whose pixel values ​​are equal to or greater than the threshold 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 contour image, and generates 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 contour image until the width of the object region reaches a 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 extraction image generating process (S29), the shape detecting section 50 generates an extraction image in which skeletons drawn in the skeleton image and having a length greater than a threshold are extracted.

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

[0078] This is the end of the flow chart shown in Fig. 4. By executing the flow chart shown in Fig. 4, information on 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 include a background processing module and a shape detection module. The main module is a part that controls the device as a whole. Functions realized by executing the acquisition module, the contour generation module, the skeleton generation module, the background processing module, and the shape detection module are the same as the functions of the acquisition unit 10, the background processing unit 20, the contour extraction unit 30, the skeleton extraction unit 40, the shape detection unit 50, and the 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 cell 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, pixels of the object 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 is 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 an area including the center of a 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)). Therefore, the background signal of the image is removed, and the skeleton showing the vascular network structure can be extracted more clearly.

[0085] In addition, the image processing method according to this embodiment is executed after a step of generating a skeleton image (skeleton image generation process (S25)), and includes a step of generating an extracted image in which skeletons drawn in the skeleton image whose length is greater than a threshold value 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 contour image generating step (contour image generating 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 according to 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 colored by the coloring step, thereby making it possible to accurately grasp the shape of the vascular network structure for each 3D liver model by executing the coloring process (S11) and the imaging process (S13) on the 3D liver model having a vascular network structure.

[0088] In addition, the image processing method according to this embodiment targets a 3D liver model having vascular endothelial cells, thereby making it possible to extract a vascular network structure in the vascular endothelial cells and detect 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. EXAMPLES

[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 of a control sample in which cyclophosphamide was not administered to a 3D liver model were obtained. In the following, unless otherwise specified, each process in the flow charts shown in Figures 3 and 4 is executed on the captured image.

[0091] [Effect of contour image generation processing] In the flowchart shown in Fig. 4, the skeletons obtained as a result of the contour image generation process (S23) were compared with those obtained when the contour image generation process (S23) was not executed (or when another process was executed). 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 the skeleton image.

[0093] Comparative Example 2 FIG. 5(c) is an image obtained by performing binarization processing on the image shown in FIG. 5(a) by setting a threshold value for pixel values ​​instead of performing the contour image generation processing (S23), displaying only pixels equal to or greater than the threshold value in white, and displaying pixels less than the threshold value in black. FIG. 5(d) is an image (Comparative Example 2) obtained by performing the skeleton image generation processing (S25) on the image shown in FIG. 5(c) as the original image. As shown in FIG. 5(d), a skeleton that matches the original image to some extent was extracted. However, for example, in an area showing a small cavity in the vascular network structure of the original image, the skeleton was not extracted accurately, and a skeleton pattern that was significantly more complicated than the corresponding part in the original image was formed. This is considered to be due to the presence of a hollow part on the pattern generated by the binarization processing. The skeleton image generation processing is a processing that erodes an object recognized in the image from the edge side until the width of the object is one pixel, but the original image shown in FIG. 5(c) has small black dots (holes) in various places in 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 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 that matches the original image is extracted, and in particular, a skeleton that accurately represents a small-scale cavity in the vascular network structure of the original image is extracted. Thus, it was confirmed that in extracting a skeleton in the skeleton image generation process, it is not necessary to correctly capture the blood vessel region of the original image, and it is sufficient to extract an approximate outline.

[0095] As described above, from Example 1, Comparative Example 1, and Comparative Example 2, it was confirmed that by executing 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 differentiate from the mesh-like parts in the original image by definition, and it is difficult to mechanically fill them in by an algorithm. In addition, it is possible to set a threshold value and perform binarization processing so that the small black dots in Comparative Example 2 are not formed, but this would result in more noise and would be unrealistic. From Example 1 and Comparative Example 2, it was confirmed that the problems caused by the small black dots described above can be solved by a new approach of extracting the skeleton after extracting the approximate contour of the original image.

[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) are diagrams showing examples of images obtained by the gradation conversion process, the noise removal process, the contour image generation process, and the skeleton image generation process. Fig. 7 (a) to (d) are diagrams showing examples of images obtained by the pixel value adjustment process, the contour image generation process, and the skeleton image generation process.

[0097] Comparative Example 3 FIG. 6(a) is a gradation image generated by the gradation conversion process (S17). FIG. 6(b) is a removed image obtained by performing the noise removal process (S19) on the gradation 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, the vascular network structure connected to other vascular networks was displayed even in the area where the vascular network structure was not clearly visible in the gradation image, and the contour and skeleton of the vascular network structure were not properly extracted as a contour image. This is considered to be due to the fact that the actual vascular region and noise were not distinguished from each other and were extracted as a contour in the contour image generation process (S23).

[0098] Example 2 FIG. 7(a) is an image in a state where the distribution of pixel values ​​is adjusted 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 executing the contour image generation process (S23) on the image shown in FIG. 7(b). FIG. 7(d) is an image (Example 2) obtained by executing the skeleton image generation process (S25) on the contour image shown in FIG. 7(c).

[0099] As shown in (a) and (b) of Fig. 7, it was confirmed that noise was removed by performing the pixel value adjustment process (S21). As shown in (d) of Fig. 7, compared with Comparative Example 3 shown in (d) of Fig. 6, the display of a vascular network structure connected to other vascular networks in an area where the vascular network structure is not clearly visible in the gradation 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] [Effects of extraction image generation process] Next, the skeletons obtained when the extraction image generation process (S29) is executed are compared with those obtained when the extraction image generation process (S29) is not executed. Fig. 8(a) is a captured image. Fig. 8(b) is a skeleton image before the extraction image generation process (S29) is executed (Comparative Example 4). Fig. 8(c) is an extraction image after the extraction image generation process (S29) is executed (Example 3).

[0101] By comparing (b) and (c) of Figure 8, when the extraction image generation process (S29) was performed, skeletons with small 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 appropriately extracted. From the above, it was found that the skeleton of the vascular network structure in the image was more appropriately extracted by performing the extraction image generation process (S29).

[0102] [Quantitative evaluation of vascular networks] A quantitative evaluation of the vascular network of a sample group in which cyclophosphamide was exposed at various concentrations to a 3D liver model (cell structure) produced in the production process described in the embodiment will be described. (a) to (f) in Fig. 9 are images acquired according to the flowchart shown in Fig. 3, and the dosage concentrations of cyclophosphamide are 0 μM, 143 μM, 430 μM, 1430 μM, 4300 μM, and 14300 μM, respectively. (g) to (l) in Fig. 9 are extracted images obtained as a result of executing each process in the flowchart shown in Fig. 4 on the images shown in (a) to (f) in Fig. 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 become shorter as the cyclophosphamide concentration increases.

[0104] FIG. 10(a) and (b) show the results of extracting the skeleton length and the number of branching points in the extracted images shown in FIG. 9(g) to (l). FIG. 10(a) shows the correspondence between the skeleton length in the extracted image and the dosage concentration of cyclophosphamide in the embodiment, and FIG. 10(b) shows the correspondence between the number of branching points of the skeleton in the extracted image and the dosage concentration of cyclophosphamide. As shown in FIG. 10(a) and (b), the shape of the vascular network structure changes significantly at the stage where the dosage concentration of cyclophosphamide exceeds a certain concentration, and the vascular network structure is reduced. Therefore, it was confirmed that the extracted image 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 contours of the vascular network structure are extracted; a step of repeatedly excluding pixels of the object from a 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; a step of generating an extracted image, the extracted image being executed after the step of generating the skeleton image, in which the skeleton drawn in the skeleton image has a length greater than a threshold value; 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 a 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 a background signal of the image has been removed, the step being executed after the acquiring step and before the step of generating a contour image.

6. The image processing method according to any one of claims 1 to 5, further comprising a step of converting the image into an image of a predetermined gradation, the step being executed after the acquiring step and before the step of generating the contour image.

7. a step of immunostaining cells having the vascular network structure, fluorescently modifying cells having the vascular network structure in advance, or coloring the vascular network structure by a combination of immunostaining and fluorescent modification, the step being performed before the step of obtaining 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 claim 1 , wherein the cell structure comprises a vascular endothelial cell.

Images