Transmission electron microscope identification method and system in gastric cancer tumor tissue exosome separation process

Abstract

The invention relates to the field of image recognition, and provides a transmission electron microscope identification method and system for a gastric cancer tumor tissue exosome separation process, and the method comprises the steps: dividing a suspected structure body in a target lens image through a clustering algorithm, carrying out the primary evaluation of the expression degree of an edge exosome based on the edge discrimination degree and the edge outer side darkness degree of the suspected structure body, and carrying out the secondary evaluation of the expression degree of the edge exosome; secondary evaluation is carried out in combination with the background staining change state of the suspected structural body, so that the influence of low distinction degree between the impurities and the exosomes caused by deep outlines of the impurities in the background part in the prior art is avoided, the identification accuracy is improved, the exosome form matching degree is calculated according to the internal depression characteristics of the suspected structural body, and the identification accuracy is improved. The problem that the accuracy of an identification result is affected due to the fact that the direct facing conditions of different structural bodies in a lens image and an observation view angle are different is solved, auxiliary analysis of the indentation characteristics of the exosome under the direct facing condition of different view angles is achieved, and the accuracy of transmission electron microscope identification in the separation process of the exosome of the gastric cancer tumor tissue is improved.

Description

Technical Field

[0001] The present invention relates to the field of image recognition, and particularly to a transmission electron microscope identification method and system for the isolation process of exosomes in gastric cancer tumor tissues. Background Art

[0002] With the rapid development of tumor immunology research, it is speculated that gastric cancer cells carry lncRNA-ZFAS1 to DC cells through exosomes and regulate their differentiation, thereby inhibiting immune responses and promoting the immune escape of tumor cells. Therefore, in order to determine the specific expression of exosomes in tumor-related regions of gastric cancer patients, it is crucial to isolate the exosomes in gastric cancer tumor tissues and observe and identify them through a transmission electron microscope.

[0003] In the prior art, during the process of identifying exosomes through a transmission electron microscope, negative staining of exosomes is first required to improve the distinguishability between exosomes and the background in the lens image. At the same time, based on the fact that the size of exosomes is usually in the range of 30 to 150 nm and their morphological size characteristics often present as concave hemispherical shapes, the size information and morphological information of each structure in the lens image are screened and identified. However, in the actual exosome identification process, it is often possible that the contours of some impurities in the background are relatively deep, resulting in a low distinguishability between them and exosomes, which affects the identification accuracy of exosomes. At the same time, there are differences in the alignment of different structures in the lens image with the observation perspective, and the higher the perspective alignment rate of a structure, the higher the confidence level corresponding to the analysis of its morphological characteristics. Therefore, it further leads to a low accuracy of the identification results obtained by using the perspective confidence consistency in the traditional exosome identification process.

[0004] Therefore, how to design a transmission electron microscope identification method for the isolation process of exosomes in gastric cancer tumor tissues to improve the accuracy of the identification results has become an urgent problem to be solved. Summary of the Invention

[0005] Based on this, a transmission electron microscope identification method and system for the isolation process of exosomes in gastric cancer tumor tissues proposed by the present invention divides the suspected structures in the target lens image through a clustering algorithm, and conducts a primary evaluation of the exosome manifestation degree on the edge based on the edge distinguishability degree and the degree of darkness outside the edge of the suspected structure. Then, a secondary evaluation is combined with the background staining change state of the suspected structure, avoiding the influence that the contours of some impurities in the background are relatively deep in the prior art, resulting in a low distinguishability between impurities and exosomes, and improving the identification accuracy. Then, the exosome morphology matching degree is calculated according to the internal concave characteristics of the suspected structure, avoiding the problem that the accuracy of the identification results is affected by the difference in the alignment of different structures in the lens image with the observation perspective, and realizing the auxiliary analysis of the concave characteristics of exosomes under different perspective alignment conditions. The present invention improves the accuracy of the transmission electron microscope identification in the isolation process of exosomes in gastric cancer tumor tissues.

[0006] A transmission electron microscope identification method for the separation process of exosomes from gastric cancer tumor tissues proposed by the present invention includes: Obtain a target lens image, screen the target lens image according to a clustering algorithm to obtain suspected structures, and perform a primary evaluation process based on the edge characteristics of the suspected structures to obtain the degree of exosome manifestation at the edge. The primary evaluation process is based on the edge differentiation degree and the degree of darkness on the outer side of the edge of the suspected structure; Perform a secondary evaluation process based on the background staining change state of the suspected structure to obtain the degree of exosome tendency. The background staining change state is based on the staining gradient rule in the vicinity of the suspected structure; Perform exosome morphology matching calculation based on the internal depression characteristics of the suspected structure to obtain the exosome morphology matching degree; Calculate an exosome identification index based on the exosome morphology matching degree to obtain an exosome identification result.

[0007] In summary, according to the above method for identifying exosomes in gastric cancer tumor tissue separation process by transmission electron microscopy, the suspected structures in the target lens image are divided by a clustering algorithm, and the performance degree of exosomes at the edge is evaluated once based on the edge differentiation degree and the degree of darkness outside the edge of the suspected structure. Then, a secondary evaluation is carried out in combination with the background staining change state of the suspected structure, avoiding the influence of the relatively deep contour of impurities in the background part in the prior art, which leads to low differentiation between impurities and exosomes, and improving the accuracy of identification. Furthermore, the morphological matching degree of exosomes is calculated according to the internal depression characteristics of the suspected structure, avoiding the problem that the accuracy of the identification result is affected by the difference in the facing situation of different structures and the observation angle in the lens image, and realizing the auxiliary analysis of the depression characteristics of exosomes under different facing situations of the observation angle. The present invention improves the accuracy of identifying exosomes in gastric cancer tumor tissue separation process by transmission electron microscopy. Specifically, a target lens image is obtained, the target lens image is screened according to a clustering algorithm to obtain suspected structures, and a primary evaluation process is carried out according to the edge characteristics of the suspected structures to obtain the performance degree of exosomes at the edge. The primary evaluation process is based on the edge differentiation degree and the degree of darkness outside the edge of the suspected structure. A secondary evaluation process is carried out according to the background staining change state of the suspected structure to obtain the tendency degree of exosomes. The background staining change state is based on the staining gradient law in the vicinity of the suspected structure, avoiding the influence of the relatively deep contour of impurities in the background part in the prior art, which leads to low differentiation between impurities and exosomes, and improving the accuracy of identification. The morphological matching calculation of exosomes is carried out according to the internal depression characteristics of the suspected structure to obtain the morphological matching degree of exosomes, avoiding the problem that the accuracy of the identification result is affected by the difference in the facing situation of different structures and the observation angle in the lens image, and realizing the auxiliary analysis of the depression characteristics of exosomes under different facing situations of the observation angle. The exosome identification index is calculated according to the morphological matching degree of exosomes to obtain the exosome identification result. The present invention improves the accuracy of identifying exosomes in gastric cancer tumor tissue separation process by transmission electron microscopy.

[0008] Further, the step of obtaining the target lens image specifically includes: Collect the peripheral blood related to the target tumor tissue, separate the serum exosomes by an exosome isolation kit, and resuspend the obtained exosomes with a culture medium to obtain a serum exosome sample. Use a pipette to drop a serum exosome sample liquid with a first preset volume threshold onto a copper mesh of a transmission electron microscope and let it stand for a first preset time. Then, take a dye solution with a first preset volume threshold and drop it onto the copper mesh of the transmission electron microscope to perform a staining operation on the serum exosome sample. The staining operation lasts for a second preset time. After staining is completed, turn on the transmission electron microscope to collect an exosome lens image, and perform a grayscale conversion process to obtain a target lens image and upload the target lens image to a data acquisition center, which is used to temporarily store the target lens image before identification.

[0009] Further, the step of screening the target lens image according to the clustering algorithm to obtain suspected structures and performing a primary evaluation process according to the edge features of the suspected structures to obtain the degree of exosome manifestation on the edge specifically includes: Perform Kmeans clustering processing based on the coordinate values and gray values of the pixel points in the target lens image. In the Kmeans clustering processing, determine the number of clusters according to the elbow method, and divide the target lens image into multiple clusters; Take each of the clusters as a single suspected structure, and respectively extract the outer edge lines of each of the suspected structures. Calculate the gradient mean of the outer edge line of each suspected structure and the gradient mean of all pixel points in the target lens image according to the Sobel operator; Calculate the edge discrimination degree of the suspected structure according to the gradient mean of the outer edge line of the suspected structure and the gradient mean of all pixel points in the target lens image. The edge discrimination degree is positively correlated with the gradient mean of the outer edge line of the suspected structure; Calculate the degree of exosome manifestation on the edge of the suspected structure according to the degree of darkness on the outer side of the edge of the suspected structure.

[0010] Further, the step of calculating the degree of exosome manifestation on the edge of the suspected structure according to the degree of darkness on the outer side of the edge of the suspected structure specifically includes: Define the adjacent range of each suspected structure according to a preset adjacent step length. The adjacent range is the spatial range within a preset adjacent step length from the outside of the suspected structure. If the distance between suspected structures is less than or equal to the preset adjacent step length, the adjacent range is based on the midpoint of the connection line of the centers of the suspected structures; Calculate the gray mean value within the adjacent range of a single suspected structure and the gray mean value within the adjacent ranges of all suspected structures, so as to calculate the degree of darkness on the outer side of the edge of the suspected structure according to the gray mean value within the adjacent range of the single suspected structure and the gray mean value within the adjacent ranges of all suspected structures, and then calculate the degree of exosome manifestation on the edge of the suspected structure according to the degree of darkness on the outer side of the edge of the suspected structure.

[0011] Further, the step of performing a secondary evaluation process according to the background staining change state of the suspected structure to obtain the degree of exosome tendency specifically includes: Connect the centers of each suspected structure to the outermost pixels in the adjacent range to generate multiple dyeing gradient paths of the suspected structures. Take the Euclidean distance between the pixels in each dyeing gradient path and the center of the suspected structure as the abscissa value, and take the gray value of the pixels in each dyeing gradient path as the ordinate value to generate the dyeing gradient curve corresponding to the suspected structure. Each suspected structure includes multiple dyeing gradient curves, and each dyeing gradient path has a uniquely corresponding dyeing gradient curve; Determine whether there is any pixel on each dyeing gradient curve whose gray value is lower than that of the adjacent previous pixel. If there is any pixel whose gray value is lower than that of the adjacent previous pixel, determine that the pixel is an inverse-order pixel; Calculate the number of inverse-order pixels contained in each suspected structure, calculate the degree of violation of the dyeing rule corresponding to the suspected structure according to the number of inverse-order pixels, and then calculate the exosome tendency degree of the suspected structure according to the degree of violation of the dyeing rule.

[0012] Further, the step of performing exosome morphology matching calculation according to the internal depression characteristics of the suspected structure to obtain the exosome morphology matching degree specifically includes: Extract all the strong edge lines inside each suspected structure according to the Canny edge detection algorithm. When the viewing angle is directly facing, the strong edge line is a single complete circular-connected domain. When there is a non-direct viewing angle deviation, the strong edge line is a part of a single complete circular-connected domain; Select the suspected structures with a single strong edge line. Start displacement from any endpoint of the strong edge line, and pass through all the points on the strong edge line completely. Calculate the Euclidean distance from the geometric center of the suspected structure to each point on the strong edge line. Take the displacement amount as the abscissa value and the Euclidean distance as the ordinate value to generate the internal edge analysis curve of the suspected structure; Connect the geometric center of the suspected structure to the two endpoints of the strong edge line and extend them to intersect with the outer edge of the suspected structure. Extract the obtained sector-like ring region. Calculate the standard deviation of the corresponding internal edge analysis curve of the suspected structure. Extract the outer edge line of the sector-like ring region, calculate the external edge analysis curve of the outer edge line of the sector-like ring region, and calculate the standard deviation of the external edge analysis curve; Judge the confidence level of the edge smoothness feature according to the standard deviation of the internal edge analysis curve and the standard deviation of the external edge analysis curve. If the confidence level of the edge smoothness feature meets the confidence level requirement, calculate the degree of conformity of the depression feature of the suspected structure; Calculate the exosome morphology matching degree of the suspected structure according to the degree of conformity of the depression feature of the suspected structure.

[0013] Further, the step of calculating the exosome discrimination index based on the exosome morphology matching degree to obtain the exosome discrimination result specifically includes: Calculating the exosome discrimination index according to the exosome morphology matching degree, and then grading the exosome discrimination index; If the exosome discrimination index is less than the first preset index threshold, it is determined that the current suspected structure is not an exosome; If the exosome discrimination index is greater than the second preset index threshold, it is determined that the current suspected structure is the exosome body; If the exosome discrimination index is greater than or equal to the first preset index threshold and less than or equal to the second preset index threshold, the current suspected structure is placed in the list of structures to be observed; When the number of suspected structures in the list of structures to be observed is greater than or equal to 50% of the number of suspected structures, the resolution of the transmission lens is adjusted and the lens image is collected again. If the number of suspected structures in the list of structures to be observed is still greater than or equal to 50% of the number of suspected structures after the third identification, the exosomes in the current discrimination area are observed manually.

[0014] A transmission electron microscopy discrimination system for the separation process of exosomes from gastric cancer tumor tissues proposed by the present invention includes: A primary evaluation module, configured to obtain a target lens image, screen the target lens image according to a clustering algorithm to obtain suspected structures, and perform a primary evaluation process on the suspected structures according to the edge features of the suspected structures to obtain the degree of exosome manifestation at the edge. The primary evaluation process is based on the edge differentiation degree and the degree of darkness on the outer side of the edge of the suspected structure; A secondary evaluation module, configured to perform a secondary evaluation process according to the background staining change state of the suspected structure to obtain the degree of exosome tendency. The background staining change state is based on the staining gradient rule in the vicinity of the suspected structure; A morphology matching degree calculation module, configured to perform exosome morphology matching calculation according to the internal depression characteristics of the suspected structure to obtain the exosome morphology matching degree; An index discrimination module, configured to calculate an exosome discrimination index according to the exosome morphology matching degree to obtain an exosome discrimination result.

[0015] The present invention also provides a storage medium storing one or more programs, and when the programs are executed by a processor, the transmission electron microscopy discrimination method for the separation process of exosomes from gastric cancer tumor tissues as described above is implemented.

[0016] The present invention also provides a computer device, which includes a memory and a processor, wherein: The memory is used to store a computer program; When the processor is used to execute the computer program stored in the memory, the method for identifying the transmission electron microscope of the exosome separation process of gastric cancer tumor tissue as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 FIG. 6 is a flowchart of a method for identifying the transmission electron microscope of the exosome separation process of gastric cancer tumor tissue according to the first embodiment of the present invention; Figure 2 FIG. 9 is a schematic structural diagram of a system for identifying the transmission electron microscope of the exosome separation process of gastric cancer tumor tissue according to the second embodiment of the present invention.

[0018] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. SPECIFIC EMBODIMENTS

[0019] For the convenience of understanding the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be a middle element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a middle element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] Please refer to Figure 1 , which shows a flowchart of a method for identifying the transmission electron microscope of the exosome separation process of gastric cancer tumor tissue according to the first embodiment of the present invention. The method for identifying the transmission electron microscope of the exosome separation process of gastric cancer tumor tissue includes steps S01 to S04, wherein: Step S01: Obtain a target lens image, screen the target lens image according to a clustering algorithm to obtain a suspected structure, and perform a primary evaluation process according to the edge characteristics of the suspected structure to obtain the degree of exosome manifestation at the edge; It should be noted that in this embodiment, the primary evaluation process is based on the edge differentiation degree and the degree of darkness outside the edge of the suspected structure. Peripheral blood related to the target tumor tissue is collected, serum exosomes are separated by an exosome isolation kit, and the obtained exosomes are resuspended with a culture medium to obtain a serum exosome sample. A serum exosome sample liquid with a first preset volume threshold is dropped onto a copper grid of a transmission electron microscope by a pipette and left standing for a first preset time. Then, a first preset volume threshold of staining solution is dropped onto the copper grid of the transmission electron microscope to perform a staining operation on the serum exosome sample. The staining operation lasts for a second preset time. After the staining is completed, the transmission electron microscope is turned on to collect an exosome lens image, and a grayscale conversion process is performed to obtain a target lens image and upload the target lens image to a data acquisition center, which is used to temporarily store the target lens image before identification; In this embodiment, the first preset volume threshold is 10 μL, the first preset time is 2 min, and the second preset time is 1.5 min; Since the exosome contour is clear and the differentiation from the external background is large, clustering processing is performed on the lens image to obtain each suspected structure. Furthermore, because the essence of negative staining is to color the background around the exosome, thus highlighting the morphological structure of the exosome, this will cause the edge of the exosome in the lens image to be deepened while the gray scale of the background around the exosome becomes darker. Therefore, the edge differentiation degree and the degree of darkness outside the edge of each suspected structure are analyzed to evaluate the performance degree of the exosome at the edge; According to the coordinate values and gray scale values of the pixel points in the target lens image, Kmeans clustering processing is performed. In the Kmeans clustering processing, the number of clusters is determined according to the elbow method, and the target lens image is divided into multiple clusters; Each cluster is used as a single suspected structure, and the outer edge line of each suspected structure is extracted respectively. According to the Sobel operator, the gradient mean value of the outer edge line of each suspected structure and the gradient mean value of all pixel points in the target lens image are calculated; The edge differentiation degree of the suspected structure is calculated according to the gradient mean value of the outer edge line of the suspected structure and the gradient mean value of all pixel points in the target lens image. The edge differentiation degree is positively correlated with the gradient mean value of the outer edge line of the suspected structure; The performance degree of the exosome at the edge of the suspected structure is calculated according to the degree of darkness outside the edge of the suspected structure; If the gradient mean value level of the edge line of the suspected structure is higher, it indicates that the differentiation degree between the suspected structure and the background is higher, and the suspicion degree of the exosome is higher. The calculation formula for the edge differentiation degree of the suspected structure in this embodiment is as follows: , where, represents the edge differentiation degree of the suspected structure, represents the gradient mean of the outer edge line of the suspected structure, and represents the gradient mean of all pixel points in the target lens image; Define the adjacent range of each suspected structure according to the preset adjacent step length. The adjacent range is the spatial range within one preset adjacent step length outside the suspected structure. If the distance between suspected structures is less than or equal to the preset adjacent step length, the adjacent range is based on the midpoint of the line connecting the centers of the suspected structures; Calculate the gray-scale mean within the adjacent range of a single suspected structure and the gray-scale mean within the adjacent ranges of all suspected structures, so as to calculate the degree of darkness outside the edge of the suspected structure based on the gray-scale mean within the adjacent range of the single suspected structure and the gray-scale mean within the adjacent ranges of all suspected structures, and then calculate the degree of exosome manifestation outside the edge of the suspected structure according to the degree of darkness outside the edge of the suspected structure; Since negative staining treatment will make the gray scale of the background in the adjacent area of exosomes darker, the degree of exosome manifestation outside the edge of the suspected structure is calculated in combination with the degree of darkness outside the edge; Since the staining degree of each exosome by the staining treatment has little difference, the smaller the difference between the gray-scale means within the adjacent ranges of a certain suspected structure and other suspected structures, and the smaller the gray-scale mean within the adjacent range of this suspected structure, the greater the probability of exosomes in this suspected structure; If the degree of edge differentiation of the suspected structure is greater and the degree of darkness outside the edge is higher, it indicates that the possibility of this suspected structure being an exosome is greater; In this embodiment, the preset adjacent step length is 10 pixel distances, and the calculation formula for the degree of darkness outside the edge of the suspected structure is as follows: , where, represents the degree of darkness outside the edge of the suspected structure, represents function, represents the gray-scale mean within the adjacent range of a single suspected structure, represents the gray-scale mean within the adjacent ranges of all suspected structures; Define the adjacent range of each suspected structure according to the preset adjacent step length. The adjacent range is the spatial range within one preset adjacent step length outside the suspected structure. If the distance between suspected structures is less than or equal to the preset adjacent step length, the adjacent range is based on the midpoint of the line connecting the centers of the suspected structures; Calculate the grayscale mean within the vicinity of a single suspected structure and the grayscale mean within the vicinity of all suspected structures, and calculate the degree of darkening outside the edge of the suspected structure based on the grayscale mean within the vicinity of the single suspected structure and the grayscale mean within the vicinity of all suspected structures. Then, calculate the degree of exosome manifestation outside the edge of the suspected structure based on the degree of darkening outside the edge of the suspected structure.

[0023] The calculation formula for the degree of exosome manifestation outside the edge of the suspected structure is as follows: , where, represents the degree of exosome manifestation outside the edge of the suspected structure.

[0024] Step S02: Perform a secondary evaluation process based on the background staining change state of the suspected structure to obtain the exosome tendency degree; It should be noted that in this embodiment, the background staining change state is based on the staining gradient law within the vicinity of the suspected structure. When considering staining the exosome, the farther away from the exosome, the lighter the staining effect and the lower the degree of influence on the background. Therefore, the exosome tendency degree of each suspected structure is obtained by combining the background staining situation; Connect the center of each suspected structure to the outermost pixel points within the vicinity to generate multiple staining gradient paths of the suspected structure. Take the Euclidean distance between the pixel points and the center of the suspected structure in each staining gradient path as the abscissa value, and take the grayscale value of the pixel points in each staining gradient path as the ordinate value to generate the staining gradient curve corresponding to the suspected structure. Each suspected structure includes multiple staining gradient curves, and each staining gradient path has a unique corresponding staining gradient curve; Determine whether there is any pixel point on each staining gradient curve whose grayscale value is lower than that of the adjacent previous pixel point. If there is any pixel point whose grayscale value is lower than that of the adjacent previous pixel point, then determine that the pixel point is an inverse-order pixel; Calculate the number of inverse-order pixels included in each suspected structure, and calculate the degree of violation of the staining law of the corresponding suspected structure based on the number of inverse-order pixels. Then, calculate the exosome tendency degree of the suspected structure based on the degree of violation of the staining law; Negative staining should make the pixel grayscale within the vicinity of the suspected structure gradually decrease as the distance from the suspected structure increases. Therefore, the more the number of inverse-order pixels of a certain suspected structure, the less it satisfies this change law, indicating that the possibility of the suspected structure being a stained exosome is lower; The calculation formula for the degree of violation of the staining law of the suspected structure is as follows: , where, Indicates the degree of violation of the coloring law of the suspected structure, Indicates the number of pixels in reverse order; If the suspected structure violates the staining rules less and the marginal exosome expression is higher, it means that the suspected structure has a higher exosome tendency; The calculation formula for the exosome tropism of suspected structures is as follows: , in, Indicates the degree of exosome tropism of the suspected structure.

[0025] Step S03: performing exosome morphology matching calculation according to the internal concave features of the suspected structure to obtain the exosome morphology matching degree; It should be noted that in this embodiment, since the exosomes have a concave hemispherical feature that tends to be concave toward the center, the deep edge performance inside the suspected structure is used to determine whether the interior has a concave feature, and then the suspected structure with the concave feature is screened out, and then the degree of concave tendency to the center is combined with the visual angle of the exosome to obtain the degree of conformity of the concave feature of the exosome, and then the degree of conformity of the concave feature and the degree of exosome tendency are combined to obtain the exosome morphological matching degree of each suspected structure; Because the morphology of exosomes is often a single concave hemisphere, the strong edge line representing the concave edge of the exosome under the transmission electron microscope should be a single line, and this strong edge line is a complete quasi-circular connected domain when the viewing angle is facing the right angle. When the viewing angle is not facing the right angle, this strong edge line should be part of the complete quasi-circular connected domain. According to the Canny edge detection algorithm, the strong edge lines inside all suspected structures are extracted. When the viewing angle is facing, the strong edge lines are a single complete quasi-circular connected domain. When the viewing angle is not facing, the strong edge lines are part of a single complete quasi-circular connected domain. Screening suspected structures with a single strong edge line, starting displacement from any end point of the strong edge line, completely passing through all points on the strong edge line, calculating the Euclidean distance from the geometric center of the suspected structure to each point on the strong edge line, using the displacement as the abscissa value and the Euclidean distance as the ordinate value, to generate an internal edge analysis curve of the suspected structure; Different from the irregular growth changes of other concave boundaries, the strong edge line of the concave boundary inside the exosome presents a relatively smooth feature. Therefore, the geometric center of the suspected structure is connected to the two end points of the strong edge line and extended to intersect with the outer edge of the suspected structure to extract the fan-shaped ring area. Connect the geometric center of the suspected structure to the two end points of the strong edge line and extend it to intersect with the outer edge of the suspected structure, extract a quasi-sector ring area, calculate the standard deviation of the internal edge analysis curve corresponding to the suspected structure, extract the outer edge line of the quasi-sector ring area, calculate the external edge analysis curve of the outer edge line of the quasi-sector ring area, and calculate the standard deviation of the external edge analysis curve; The confidence of the edge smoothing feature is determined according to the standard deviation of the internal edge analysis curve and the standard deviation of the external edge analysis curve, and if the confidence of the edge smoothing feature meets the confidence requirement, the degree of conformity of the concave feature of the suspected structure is calculated; Calculating the exosome morphology matching degree of the suspected structure according to the degree of conformity of the concave features of the suspected structure; Because the strong edge line of the concave boundary inside the exosome presents a relatively smooth feature, and the higher the viewing angle is, the higher the confidence of the concave feature is, and the smaller the standard deviation of the internal edge analysis curve is, the smoother the strong edge line inside the suspected structure is, the greater the probability of the suspected structure being an exosome; The closer the standard deviation corresponding to the outer edge of the suspected structure is to the standard deviation corresponding to the inner edge, the higher the consistency of the change between the inner edge line of the suspected structure and the nearest outer boundary edge, which means that the suspected structure is more likely to be facing the view angle, and the more concave information appears in the suspected structure at this time, so it can be used as the confidence of the edge smoothing feature. At the same time, if the smoothing feature is smaller, it means that the concave feature of the suspected structure is more consistent with the exosome feature. If the suspected structure has a greater tendency toward exosomes and a higher degree of concave feature conformity, it further indicates that the suspected structure has a higher degree of exosome matching; The calculation formula for the degree of conformity of the concave features of the suspected structure is as follows: , in, Indicates the degree of conformity of the concave features of the suspected structure. represents the standard deviation of the internal edge analysis curve corresponding to the suspected structure. In this embodiment, the standard deviation of the internal edge analysis curve corresponding to the suspected structure is used as the edge smoothing feature. represents the standard deviation of the outer edge analysis curve; The calculation formula for the exosome morphology matching of suspected structures is as follows: , in, Indicates the degree of exosome morphology matching of the suspected structure.

[0026] Step S04: calculating the exosome identification index according to the exosome morphology matching degree to obtain the exosome identification result; It should be noted that in this embodiment, since the size standard of exosomes is 30 to 150 nm, the deviation degree between the size of the suspected structure and the standard size is combined, and the exosome identification index is calculated according to the morphological matching degree of exosomes, and then the exosome identification index is graded; If the exosome identification index is less than the first preset index threshold, it is determined that the current suspected structure is not an exosome; If the exosome identification index is greater than the second preset index threshold, it is determined that the current suspected structure is the exosome body; If the exosome identification index is greater than or equal to the first preset index threshold and less than or equal to the second preset index threshold, the current suspected structure is placed in the list to be observed; When the number of suspected structures in the list to be observed is greater than or equal to 50% of the number of suspected structures, the resolution of the transmission lens is adjusted and the lens image is collected again. If the number of suspected structures in the list to be observed is still greater than or equal to 50% of the number of suspected structures after the third identification, the exosomes in the current identification area are observed manually; The calculation formula of the exosome identification index is as follows: , where, represents the exosome identification index, represents the maximum inner diameter length of the suspected structure, represents the reference diameter length, and the reference diameter length in this embodiment is 90; In this embodiment, the first preset index threshold is 0.33 and the second preset index threshold is 0.88.

[0027] In summary, according to the above method for identifying exosomes in gastric cancer tumor tissue separation process by transmission electron microscopy, the suspected structures in the target lens image are divided by a clustering algorithm, and the performance degree of exosomes at the edge is evaluated once based on the edge differentiation degree and the degree of darkness outside the edge of the suspected structure. Then, a secondary evaluation is carried out in combination with the background staining change state of the suspected structure, avoiding the influence of the relatively deep contour of impurities in the background part in the prior art, resulting in low differentiation between impurities and exosomes, and improving the accuracy of identification. Furthermore, the morphological matching degree of exosomes is calculated according to the internal depression characteristics of the suspected structure, avoiding the problem that the accuracy of the identification result is affected by the difference in the facing situation between different structures and the observation angle in the lens image, realizing the auxiliary analysis of the depression characteristics of exosomes under different facing situations of the observation angle. The present invention improves the accuracy of identifying exosomes in gastric cancer tumor tissue separation process by transmission electron microscopy. Specifically, a target lens image is obtained, the target lens image is screened according to a clustering algorithm to obtain suspected structures, a primary evaluation process is carried out according to the edge characteristics of the suspected structures to obtain the performance degree of exosomes at the edge, the primary evaluation process is based on the edge differentiation degree and the degree of darkness outside the edge of the suspected structure, a secondary evaluation process is carried out according to the background staining change state of the suspected structure to obtain the tendency degree of exosomes, the background staining change state is based on the staining gradient law in the vicinity of the suspected structure, avoiding the influence of the relatively deep contour of impurities in the background part in the prior art, resulting in low differentiation between impurities and exosomes, and improving the accuracy of identification. The morphological matching calculation of exosomes is carried out according to the internal depression characteristics of the suspected structure to obtain the morphological matching degree of exosomes, avoiding the problem that the accuracy of the identification result is affected by the difference in the facing situation between different structures and the observation angle in the lens image, realizing the auxiliary analysis of the depression characteristics of exosomes under different facing situations of the observation angle. The exosome identification index is calculated according to the morphological matching degree of exosomes to obtain the exosome identification result. The present invention improves the accuracy of identifying exosomes in gastric cancer tumor tissue separation process by transmission electron microscopy.

[0028] Please refer to Figure 2 , which shows the structural schematic diagram of the system for identifying exosomes in gastric cancer tumor tissue separation process proposed in the second embodiment of the present invention. The system includes: A primary evaluation module 10, configured to obtain a target lens image, screen the target lens image according to a clustering algorithm to obtain suspected structures, and perform a primary evaluation process according to the edge characteristics of the suspected structures to obtain the performance degree of exosomes at the edge, where the primary evaluation process is based on the edge differentiation degree and the degree of darkness outside the edge of the suspected structure; A secondary evaluation module 20, configured to perform a secondary evaluation process according to the background staining change state of the suspected structure to obtain the tendency degree of exosomes, where the background staining change state is based on the staining gradient law in the vicinity of the suspected structure; A morphology matching degree calculation module 30 is configured to perform exosome morphology matching calculation according to the internal depression features of the suspected structure to obtain an exosome morphology matching degree; An index identification module 40 is configured to calculate an exosome identification index according to the exosome morphology matching degree to obtain an exosome identification result.

[0029] The present invention also provides a computer storage medium, on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned transmission electron microscopy identification method for the separation process of exosomes from gastric cancer tumor tissues is implemented.

[0030] The present invention also provides a computer device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the above-mentioned transmission electron microscopy identification method for the separation process of exosomes from gastric cancer tumor tissues.

[0031] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0032] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0033] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques well known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0034] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0035] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy, characterized in that: include: Obtain a target lens image, screen the target lens image according to a clustering algorithm to obtain a suspected structure, and perform an evaluation process according to the edge features of the suspected structure to obtain the degree of edge exosome expression, wherein the evaluation process is based on the degree of edge distinction of the suspected structure and the degree of darkening outside the edge; Performing secondary evaluation processing according to the background staining change state of the suspected structure to obtain the degree of exosome tendency, wherein the background staining change state is based on the staining gradient law within the vicinity of the suspected structure; Performing exosome morphology matching calculation according to the internal concave features of the suspected structure to obtain the exosome morphology matching degree; The exosome identification index is calculated according to the exosome morphology matching degree to obtain the exosome identification result.

2. The method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy according to claim 1, characterized in that: The step of obtaining the target lens image specifically includes: Peripheral blood related to the target tumor tissue is collected, serum exosomes are separated by an exosome separation kit, and the obtained exosomes are resuspended in a culture medium to obtain a serum exosome sample, and a serum exosome sample of a first preset volume threshold is dropped on a copper mesh of a transmission electron microscope by a pipette and left to stand for a first preset time, and then a dye solution of a first preset volume threshold is dropped on the copper mesh of the transmission electron microscope to perform a staining operation on the serum exosome sample, and the staining operation lasts for a second preset time. After the staining is completed, the transmission electron microscope is turned on to collect an exosome lens image, and grayscale image conversion processing is performed to obtain a target lens image and upload the target lens image to a data acquisition center, and the data acquisition center is used to temporarily store the target lens image before identification.

3. The method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy according to claim 1, characterized in that: The step of screening the target lens image according to the clustering algorithm to obtain a suspected structure, and performing an evaluation process according to the edge features of the suspected structure to obtain the degree of edge exosome expression specifically includes: According to the coordinate values ​​and grayscale values ​​of the pixel points in the target lens image, Kmeans clustering processing is performed, wherein the number of clusters is determined according to the elbow method in the Kmeans clustering processing, and the target lens image is divided into a plurality of clusters; Each of the clusters is regarded as a single suspected structure, and the outer edge lines of each of the suspected structures are extracted respectively, and the gradient mean of the outer edge lines of each suspected structure and the gradient mean of all pixel points in the target lens image are calculated according to the Sobel operator; Calculating the edge distinction degree of the suspected structure according to the gradient mean of the outer edge line of the suspected structure and the gradient mean of all pixels in the target lens image, wherein the edge distinction degree is positively correlated with the gradient mean of the outer edge line of the suspected structure; The degree of exosome expression at the edge of the suspected structure is calculated according to the degree of darkening of the outer side of the edge of the suspected structure.

4. The method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy according to claim 3, characterized in that: The step of calculating the degree of exosome expression at the edge of the suspected structure according to the degree of darkening of the outer edge of the suspected structure specifically includes: Delimit the proximity range of each suspected structure according to the preset proximity step, wherein the proximity range is the spatial range within a preset proximity step from the outside of the suspected structure. If the distance between the suspected structures is less than or equal to the preset proximity step, the proximity range is based on the midpoint of the line connecting the centers of the suspected structures; The grayscale mean within the vicinity of a single suspected structure and the grayscale mean within the vicinity of all suspected structures are calculated, and the degree of darkening of the outer edge of the suspected structure is calculated according to the grayscale mean within the vicinity of the single suspected structure and the grayscale mean within the vicinity of all suspected structures, and then the degree of exosome expression at the edge of the suspected structure is calculated according to the degree of darkening of the outer edge of the suspected structure.

5. The method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy according to claim 1, characterized in that: The step of performing secondary evaluation processing according to the background staining change state of the suspected structure to obtain the degree of exosome tendency specifically includes: Connect the center of each suspected structure to the outermost circle pixel point of the adjacent range to generate multiple color gradient paths of the suspected structure, use the Euclidean distance between the pixel point in each color gradient path and the center of the suspected structure as the horizontal coordinate value, and use the gray value of the pixel point in each color gradient path as the vertical coordinate value to generate a color gradient curve corresponding to the suspected structure, each of the suspected structures includes multiple color gradient curves, and each of the color gradient paths has a unique corresponding color gradient curve; Determine whether there is any pixel point on each dyeing gradient curve whose grayscale value is lower than the grayscale value of the adjacent previous pixel point. If there is any pixel point whose grayscale value is lower than the grayscale value of the adjacent previous pixel point, determine that the pixel point is a reverse pixel; The number of reversed pixels contained in each suspected structure is calculated, and the degree of violation of the staining rule of the corresponding suspected structure is calculated according to the number of reversed pixels, and then the degree of exosome tendency of the suspected structure is calculated according to the degree of violation of the staining rule.

6. The method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy according to claim 1, characterized in that: The step of performing exosome morphology matching calculation according to the internal concave features of the suspected structure to obtain the exosome morphology matching degree specifically includes: According to the Canny edge detection algorithm, the strong edge lines inside all suspected structures are extracted. When the viewing angle is facing, the strong edge lines are a single complete quasi-circular connected domain. When the viewing angle is not facing, the strong edge lines are part of a single complete quasi-circular connected domain. Screening suspected structures with a single strong edge line, starting displacement from any end point of the strong edge line, completely passing through all points on the strong edge line, calculating the Euclidean distance from the geometric center of the suspected structure to each point on the strong edge line, using the displacement as the abscissa value and the Euclidean distance as the ordinate value, to generate an internal edge analysis curve of the suspected structure; Connect the geometric center of the suspected structure to the two end points of the strong edge line and extend it to intersect with the outer edge of the suspected structure, extract a quasi-sector ring area, calculate the standard deviation of the internal edge analysis curve corresponding to the suspected structure, extract the outer edge line of the quasi-sector ring area, calculate the external edge analysis curve of the outer edge line of the quasi-sector ring area, and calculate the standard deviation of the external edge analysis curve; The confidence of the edge smoothing feature is determined according to the standard deviation of the internal edge analysis curve and the standard deviation of the external edge analysis curve, and if the confidence of the edge smoothing feature meets the confidence requirement, the degree of conformity of the concave feature of the suspected structure is calculated; The exosome morphology matching degree of the suspected structure is calculated according to the matching degree of the concave features of the suspected structure.

7. The method for identifying exosomes from gastric cancer tumor tissue by transmission electron microscopy according to claim 1, characterized in that: The step of calculating the exosome identification index according to the exosome morphology matching degree to obtain the exosome identification result specifically includes: Calculating exosome identification indexes according to the exosome morphology matching degree, and then grading the exosome identification indexes; If the exosome identification index is less than the first preset index threshold, the current suspected structure is determined to be non-exosome; If the exosome identification index is greater than the second preset index threshold, the current suspected structure is determined to be the exosome body; If the exosome identification index is greater than or equal to the first preset index threshold and less than or equal to the second preset index threshold, the current suspected structure is placed in the list to be observed; When the number of suspected structures in the list to be observed is greater than or equal to 50% of the number of suspected structures, the resolution of the transmission lens is adjusted and the lens image is collected again. If the number of suspected structures in the list to be observed is still greater than or equal to 50% of the number of suspected structures after the third identification, the exosomes in the current identification area are manually observed.

8. A transmission electron microscopy identification system for the separation process of exosomes from gastric cancer tumor tissue, characterized in that: include: A primary evaluation module, for obtaining a target lens image, screening the target lens image according to a clustering algorithm to obtain a suspected structure, and performing a primary evaluation process according to the edge features of the suspected structure to obtain the degree of edge exosome expression, wherein the primary evaluation process is based on the degree of edge distinction of the suspected structure and the degree of darkening outside the edge; A secondary evaluation module, used to perform secondary evaluation processing according to the background staining change state of the suspected structure to obtain the degree of exosome tendency, wherein the background staining change state is based on the staining gradient law in the vicinity of the suspected structure; A morphology matching degree calculation module, used to perform exosome morphology matching calculation according to the internal concave features of the suspected structure to obtain the exosome morphology matching degree; The index identification module is used to calculate the exosome identification index according to the exosome morphology matching degree to obtain the exosome identification result.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the transmission electron microscopy identification method for the gastric cancer tumor tissue exosome separation process as described in any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the transmission electron microscopy identification method for the gastric cancer tumor tissue exosome separation process described in any one of claims 1-7.

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