Ubiquitous organ lymph node metastatic carcinoma analysis system based on joint deep learning model

Abstract

The invention provides a universal organ lymph node metastatic cancer analysis system based on a joint deep learning model, which comprises the following steps: obtaining and carrying out image segmentation and image labeling on a lymph image to obtain a domain image and an image display reaction corresponding to each image domain in the lymph image, then carrying out classification processing to obtain a corresponding image domain class, and carrying out classification processing on the corresponding image domain class; based on the class features corresponding to each image domain class, analyzing each class feature to determine abnormal information of the corresponding image domain class, determining cancer cell information contained in the lymphatic image according to the abnormal information, establishing a cancerization amount corresponding to each image domain in the lymphatic image based on the cancer cell information, and establishing a cancer cell metastasis degree of the corresponding patient based on the cancerization amount. The cancer cell information and the canceration amount corresponding to each image domain are marked in the lymphatic image, the case information corresponding to the patient is generated and displayed, the basic condition of the patient is analyzed in time after the patient is scanned, and a case with reference value is generated to provide convenience for doctors.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model. Background Art

[0002] Lymph nodes (LNs) are the core of the immune system and are secondary lymphoid organs distributed throughout the human body. Cancer in the lymph nodes either originates from the lymph nodes or, more often, from metastasis of the primary organ. Lymph nodes are usually the first metastatic site for various cancers and are crucial for tumor staging and prognosis. The extent of tumor metastasis is considered a powerful predictor of recurrence and survival. Pan-organ lymph node metastasis refers to the metastasis of cancer cells from the primary lesion through the lymphatic circulation or blood circulation to the lymph nodes of multiple organs of the body, forming metastatic cancer. This situation usually indicates that the cancer has progressed to an advanced stage and requires timely diagnosis and treatment.

[0003] Generally speaking, the analysis steps for pan-organ lymph node metastasis are: the doctor understands the patient's medical history and clinical manifestations and combines the patient's medical imaging examinations to assess the severity of the cancer. If necessary, a pathological examination is required to determine whether there is cancer cell metastasis in the lymph nodes. Obviously, although the traditional method requires a very high level of professionalism from doctors, doctors need years of practice and learning to accurately determine whether there is cancer cell metastasis in the lymph nodes. The work efficiency is greatly improved. Now a system that can assist doctors in judging the condition is needed.

[0004] Therefore, the present invention provides a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model. Summary of the invention

[0005] The pan-organ lymph node metastasis cancer analysis system of the present invention is based on a joint deep learning model. It can timely analyze the basic condition of the patient after the patient has completed the scan and generate a case with reference value to provide convenience for doctors.

[0006] The present invention provides a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model, comprising:

[0007] An image segmentation and annotation module is used to obtain and perform image segmentation and image annotation on the lymphatic image, and obtain a domain image and image display response corresponding to each image domain in the lymphatic image;

[0008] A domain image classification module, used to classify the image domain based on the image display response to obtain a corresponding image domain class based on a class feature corresponding to each image domain class;

[0009] An abnormality analysis and positioning module, used to analyze each of the class features to determine abnormal information of the corresponding image domain class, and determine cancer cell information contained in the lymphatic image based on the abnormal information;

[0010] an abnormality level determination module, used to establish a cancer number corresponding to each image domain in the lymphatic image based on the cancer cell information, and to establish a cancer cell metastasis degree of the corresponding patient based on the cancer number;

[0011] The information sorting and display module is used to mark the cancer cell information and cancer variable corresponding to each image domain in the lymphatic image, generate case information of the corresponding patient and display it.

[0012] In one practicable manner,

[0013] Also includes:

[0014] A metastasis analysis and estimation module, used to establish historical metastasis information and current health information of the corresponding patient according to the case information;

[0015] The current health information is estimated to metastasize according to the historical metastasis information to obtain cancer metastasis estimation information of the corresponding patient, and the estimated information is transmitted to the information sorting and display module for display.

[0016] In one practicable manner,

[0017] The image segmentation and annotation module comprises:

[0018] A lymphatic image capturing unit, used for scanning a patient using a specified medical imaging technology to generate a lymphatic image of the patient;

[0019] A lymphatic image segmentation unit, used for performing sliding window segmentation on the lymphatic image to generate a plurality of image domains;

[0020] The lymphatic image annotation unit is used to establish image comparison samples according to preset cortical features, use the image comparison samples to compare each of the image domains respectively, and establish a domain image display response corresponding to each of the image domains according to the comparison results.

[0021] In one practicable manner,

[0022] Also includes:

[0023] A lesion pre-analysis module, used for obtaining cortical information of a corresponding image domain according to the domain image display response;

[0024] establishing an abnormal cortical grade for the corresponding patient according to the cortical information;

[0025] When the cortical abnormality level is greater than a preset level, drawing an abnormal cortical contour contained in a corresponding image domain according to the cortical information;

[0026] Locating each of the abnormal cortical contours in the lymphatic image, and acquiring the immune function corresponding to each organ in the lymphatic image, to obtain the abnormal immune function corresponding to each of the abnormal cortical contours;

[0027] The manifested lesion information of the corresponding patient is established according to the abnormal immune function.

[0028] In one practicable manner,

[0029] The domain image classification module comprises:

[0030] An image pooling unit, used to perform image pooling processing on each of the image domains using a convolutional neural network to obtain an abstract image domain corresponding to each of the image domains;

[0031] A feature generation unit, used to establish abstract description information of a corresponding image domain based on the abstract image domain, establish a pooling correction rate based on a size ratio between the abstract image domain and the corresponding image domain, and establish image description information of the corresponding image domain according to the abstract description information and the pooling correction rate;

[0032] An image classification unit, used for establishing description features of a corresponding image domain according to the image description information, classifying image domains with consistent description features into one category, and generating a corresponding image domain class;

[0033] A class feature establishment unit is used to establish basic features of the corresponding image domain class based on the descriptive features, establish an image sequence according to the image domain class, generate difference features between each sequence image and the basic features in the image sequence, use each of the difference features to correct the corresponding basic features, and generate class features of the corresponding image domain class.

[0034] In one practicable manner,

[0035] Abnormal analysis and positioning module, including:

[0036] An image analysis mapping unit, used to establish an image composition structure of each image domain class according to the class features, establish a mapping background of the corresponding image domain class according to the image composition structure, and map the class images in the same image domain class to the corresponding mapping background to obtain a corresponding mapping result;

[0037] An analysis starting point establishing unit, used to determine the characteristic prominent contour of the image in each class in the corresponding image domain class according to the class feature, locate the characteristic prominent contour in the corresponding mapping result, and establish the analysis starting point position in the mapping result according to the positioning result;

[0038] an abnormal step-by-step analysis unit, used to retrieve corresponding normal information samples based on the class features, establish a search step length according to the normal information samples, and filter the mapping results for normal information and abnormal information starting from the analysis starting point to obtain normal information and abnormal information corresponding to each mapping result;

[0039] The cancer cell determination unit is used to adjust the information parameters of the corresponding abnormal information according to the class characteristics, generate abnormal information of the corresponding image domain class, establish a cancer cell distribution list of the corresponding image domain class and cancer cell actual information of the corresponding image domain class according to the abnormal information, search for the position of the cancer cells contained in each of the lymphatic images according to the cancer cell distribution list, and establish the cancer cell information of the corresponding patient in combination with the corresponding cancer cell actual information.

[0040] In one practicable manner,

[0041] The abnormal level determination module includes:

[0042] A model building and analysis unit, used to build lymphatic information of a corresponding patient according to the lymphatic image, build a cell model of the corresponding patient according to the lymphatic information and the cancer cell information, and mark a cancer cell model domain in the cell model;

[0043] a cancer variable analysis unit, used to determine the cancerous organ corresponding to each cancer cell model domain, establish an organ model domain according to the normal organ data corresponding to each cancerous organ, integrate the organ model domain into the cell model, and determine the cancerous area corresponding to each cancerous organ according to the fusion result;

[0044] an organ function analysis unit, used to determine the influence parameters of cancer cells on the corresponding cancerous organ according to the cancerous area, and to adjust the corresponding organ model domain using the influence parameters to obtain the organ function characteristics of the corresponding patient;

[0045] The cancer cell information analysis unit is used to determine the cancer depth of the corresponding cancerous organ according to the organ function characteristics, and obtain the cancer volume corresponding to each image domain in combination with the corresponding cancer area.

[0046] In one practicable manner,

[0047] The abnormal level determination module further includes:

[0048] A cancer metastasis analysis unit is used to retrieve the case information of the patient, combine the cancer amount corresponding to each image domain, and obtain the cancer increase or decrease amount corresponding to each image domain;

[0049] The degree of cancer cell metastasis of the corresponding patient is generated according to the increase or decrease amount of canceration.

[0050] In one practicable manner,

[0051] The information sorting and display module comprises:

[0052] A common marking unit, used for marking cancer cell information and cancer quantity corresponding to each image domain in the lymphatic image to generate a diagnostic image;

[0053] A lymph node screening unit, used to establish a level screening mechanism according to a plurality of preset lymph node level information, and to screen the diagnostic image using the level screening mechanism;

[0054] a lymph node grading unit, used for obtaining the lymph nodes contained in the diagnostic image and the lymph node grade corresponding to each of the lymph nodes when the diagnostic image contains lymph nodes;

[0055] The case generation unit is used to generate case information of the corresponding patient according to the cancer cell information, cancer quantity and cancer quantity and display the case information when the diagnostic image contains lymph nodes.

[0056] In one practicable manner,

[0057] Also includes:

[0058] The suggestion generation module is used to generate suggested treatment methods based on the case information for the doctor's reference.

[0059] The beneficial effects that can be achieved by the present invention are as follows: in order to better assist doctors in diagnosis and treatment and improve the work efficiency of doctors, the lymphatic images are first segmented and labeled, so that the obtained domain images are classified, and the class features corresponding to each domain class are determined. The abnormal information of the image domain class is further determined by analyzing the class features, so that the cancer cell information contained in the lymphatic image can be basically determined based on the abnormal information, and the cancer variable and the degree of cancer cell metastasis corresponding to each image domain can be obtained through deep learning analysis. Finally, a case information is generated for the doctor's reference, so that the cancer cells can be automatically screened after the patient is scanned, reducing the workload of the doctor, and the case information is generated in a short time after the patient completes the image scan, effectively avoiding the phenomenon of misdiagnosis or taking the wrong case, making it convenient for doctors to give patients symptomatic and effective treatment, allowing doctors to better devote themselves to the treatment process and reduce the waiting time of patients.

[0060] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0061] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0063] Figure 1 Schematic diagram of the composition of a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model in an embodiment of the present invention;

[0064] Figure 2 Schematic diagram of the composition of a domain image classification module in a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model in an embodiment of the present invention;

[0065] Figure 3 Schematic diagram of a sliding window segmentation process in a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model in an embodiment of the present invention;

[0066] Figure 4 Schematic diagram of the sliding window segmentation and labeling process in the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0068] Example 1

[0069] This embodiment provides a pan-organ lymph node metastasis cancer analysis system based on a joint deep learning model. Figure 1 As shown, including:

[0070] An image segmentation and annotation module is used to obtain and perform image segmentation and image annotation on the lymphatic image, and obtain a domain image and image display response corresponding to each image domain in the lymphatic image;

[0071] A domain image classification module, used to classify the image domain based on the image display response to obtain a corresponding image domain class based on a class feature corresponding to each image domain class;

[0072] An abnormality analysis and positioning module, used to analyze each of the class features to determine abnormal information of the corresponding image domain class, and determine cancer cell information contained in the lymphatic image based on the abnormal information;

[0073] an abnormality level determination module, used to establish a cancer number corresponding to each image domain in the lymphatic image based on the cancer cell information, and to establish a cancer cell metastasis degree of the corresponding patient based on the cancer number;

[0074] The information sorting and display module is used to mark the cancer cell information and cancer variable corresponding to each image domain in the lymphatic image, generate case information of the corresponding patient and display it.

[0075] In this example, the processes of image segmentation and image annotation are semantic segmentation and semantic annotation respectively;

[0076] In this example, the domain image is a part of the lymphatic image, which is the result of image segmentation of the lymphatic image;

[0077] In this example, the process of classifying the image domain is to perform cluster analysis;

[0078] In this example, several sub-images contained in the same image domain class all have the same class feature, and each image domain class corresponds to a class feature;

[0079] In this instance, abnormal information refers to information that does not belong to normal lymph nodes in the image domain class;

[0080] In this example, the cancer cell information includes information such as the distribution and area of ​​cancer cells in the lymphatic image;

[0081] In this example, there are three levels of cancer cell metastasis: T stage (primary tumor): cancer cells are limited to the primary tumor site, N stage (lymph node metastasis): cancer cells have metastasized to nearby lymph nodes, M stage (distant metastasis): cancer cells have spread to other parts of the body, such as the liver, lungs, etc., staging stage:

[0082] In this example, each patient corresponds to a case information, and the case information is updated in real time as the patient undergoes examinations.

[0083] The working principle and beneficial effects of the above technical solution: In order to better assist doctors in diagnosis and treatment and improve the work efficiency of doctors, the lymphatic images are first segmented and labeled, so as to classify the obtained domain images and determine the class features corresponding to each domain class. The abnormal information of the image domain class is further determined by analyzing the class features. Therefore, the cancer cell information contained in the lymphatic image can be basically determined based on the abnormal information. The cancer variable and the degree of cancer cell metastasis corresponding to each image domain are further obtained through deep learning analysis. Finally, a case information is generated for the doctor's reference. In this way, the cancer cells can be automatically screened after the patient is scanned, reducing the workload of the doctor, and the case information is generated in a short time after the patient completes the image scan, effectively avoiding misdiagnosis or taking the wrong case, making it easier for doctors to give patients symptomatic and effective treatment, allowing doctors to better devote themselves to the treatment process and reduce the waiting time of patients.

[0084] Example 2

[0085] Based on Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model further includes:

[0086] A metastasis analysis and estimation module, used to establish historical metastasis information and current health information of the corresponding patient according to the case information;

[0087] The current health information is estimated to metastasize according to the historical metastasis information to obtain cancer metastasis estimation information of the corresponding patient, and the estimated information is transmitted to the information sorting and display module for display.

[0088] The working principle and beneficial effects of the above technical solution: In order to improve the practicality of the system and further reduce the workload of doctors, the patient's cancer metastasis is estimated based on the patient's case information and current health information, so that the doctor can have a clear idea and better implement effective treatment methods.

[0089] Example 3

[0090] On the basis of Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model, the image segmentation and annotation module includes:

[0091] A lymphatic image capturing unit, used for scanning a patient using a specified medical imaging technology to generate a lymphatic image of the patient;

[0092] A lymphatic image segmentation unit, used for performing sliding window segmentation on the lymphatic image to generate a plurality of image domains;

[0093] The lymphatic image annotation unit is used to establish image comparison samples according to preset cortical features, use the image comparison samples to compare each of the image domains respectively, and establish a domain image display response corresponding to each of the image domains according to the comparison results.

[0094] In this example, the specified medical imaging technology may be CT scan, MRI, PET-CT scan, or ultrasound examination;

[0095] In this example, sliding window segmentation represents the process of dividing the lymphatic image into image domains with a size of 3*3 pixels;

[0096] In this example, the preset cortical features represent the features presented when the cortical region of the lymph node launches an immune response;

[0097] In this example, the domain image display response refers to the display response of the cortical area in the domain image, including: lymph node enlargement, lymphocyte proliferation, inflammatory response, leukocyte aggregation, and lymphoid follicle expansion;

[0098] In this example, the process of sliding window segmentation is as follows: Figure 3 shown.

[0099] The working principle and beneficial effects of the above technical solution: In order to analyze the characteristics of lymphatic images simply and efficiently, a lymphatic image of the patient is generated after scanning, and then it is divided into several image domains. The patient's display response is determined by comparing the cortical features contained in each image domain, which facilitates subsequent phase domain image classification.

[0100] Example 4

[0101] Based on Example 3, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model further includes:

[0102] A lesion pre-analysis module, used for obtaining cortical information of a corresponding image domain according to the domain image display response;

[0103] establishing an abnormal cortical grade for the corresponding patient according to the cortical information;

[0104] When the cortical abnormality level is greater than a preset level, drawing an abnormal cortical contour contained in a corresponding image domain according to the cortical information;

[0105] Locating each of the abnormal cortical contours in the lymphatic image, and acquiring the immune function corresponding to each organ in the lymphatic image, to obtain the abnormal immune function corresponding to each of the abnormal cortical contours;

[0106] The manifested lesion information of the corresponding patient is established according to the abnormal immune function.

[0107] In this example, the cortical information represents the responses presented by the immune work performed by the cortical region of the lymph node, including: antigen presentation and recognition, T cell activation, B cell activation, cytokine release, proliferation, and spread;

[0108] In this example, the abnormal cortical grades include: unstimulated grade, activated grade, tumor infiltration grade, and metastatic grade;

[0109] In this example, the abnormal cortical contour represents the contour formed by the abnormal cortex in the lymphatic image;

[0110] In this example, each organ corresponds to one or more immune functions;

[0111] In this example, the dominant lesion information includes lymphadenopathy, lymphocyte proliferation, inflammatory response, leukocyte aggregation, and lymphoid follicle expansion.

[0112] The working principle and beneficial effects of the above technical solution: judging the patient's condition by analyzing cortical information can effectively further analyze the patient's immune status, and also facilitate doctors and patients to understand the actual condition.

[0113] Example 5

[0114] Based on Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model is characterized in that the domain image classification module is, Figure 2 As shown, including:

[0115] An image pooling unit, used to perform image pooling processing on each of the image domains using a convolutional neural network to obtain an abstract image domain corresponding to each of the image domains;

[0116] a feature generation unit, used to establish abstract description information of a corresponding image domain based on the abstract image domain, establish a pooling correction rate based on a size ratio between the abstract image domain and the corresponding image domain, and establish image description information of the corresponding image domain according to the abstract description information and the pooling correction rate;

[0117] An image classification unit, used for establishing description features of a corresponding image domain according to the image description information, classifying image domains with consistent description features into one category, and generating a corresponding image domain class;

[0118] A class feature establishment unit is used to establish basic features of the corresponding image domain class based on the descriptive features, establish an image sequence according to the image domain class, generate difference features between each sequence image and the basic features in the image sequence, use each of the difference features to correct the corresponding basic features, and generate class features of the corresponding image domain class.

[0119] In this instance, image pooling refers to the process of reducing the size of the image domain;

[0120] In this example, the abstract image domain represents the result of pooling the image domain, and its essence is also the image domain;

[0121] In this example, the abstract description information represents an abstract and erroneous description of the image domain;

[0122] In this example, the image description information refers to information used to represent the basic situation of the image domain;

[0123] In this example, the size ratio represents the area ratio between the abstract image domain and the corresponding image domain;

[0124] In this example, the pooling correction rate is positively correlated with the size ratio;

[0125] In this instance, the description features represent the appearance characteristics of the image domain;

[0126] In this example, the basic features represent features common to multiple image domains in the image domain class;

[0127] In this example, the image sequence is a sequence generated by arranging the images in the image domain class;

[0128] In this example, the difference feature represents the difference between the basic features of the sequence graph domain;

[0129] In this example, the number of class features is 1, and one image domain class corresponds to one class feature.

[0130] The working principle and beneficial effects of the above technical solution are as follows: in order to better analyze the features contained in each image domain, the image domain is first subjected to image pooling processing to obtain its corresponding abstract image domain, and then the abstract description features of the abstract image domain are established, combined with the size ratio between the two image domains after pooling to establish the description features of the image domain, and then the image domains with consistent description features are classified, and finally the difference features between the basic features of different sequence image domains are used to establish the class features of the classified image domains, and the pooling technology is used to simplify the structure of the image domain, so that image domains with similar features can be classified, reducing classification errors and improving the efficiency of subsequent image analysis.

[0131] Example 6

[0132] Based on Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model, the abnormal analysis and positioning module, includes:

[0133] An image analysis mapping unit, used to establish an image composition structure of each image domain class according to the class features, establish a mapping background of the corresponding image domain class according to the image composition structure, and map the class images in the same image domain class to the corresponding mapping background to obtain a corresponding mapping result;

[0134] An analysis starting point establishing unit, used to determine the characteristic prominent contour of the image in each class in the corresponding image domain class according to the class feature, locate the characteristic prominent contour in the corresponding mapping result, and establish the analysis starting point position in the mapping result according to the positioning result;

[0135] an abnormal step-by-step analysis unit, used to retrieve corresponding normal information samples based on the class features, establish a search step length according to the normal information samples, and filter the mapping results for normal information and abnormal information starting from the analysis starting point to obtain normal information and abnormal information corresponding to each mapping result;

[0136] The cancer cell determination unit is used to adjust the information parameters of the corresponding abnormal information according to the class characteristics, generate abnormal information of the corresponding image domain class, establish a cancer cell distribution list of the corresponding image domain class and cancer cell actual information of the corresponding image domain class according to the abnormal information, search for the position of the cancer cells contained in each of the lymphatic images according to the cancer cell distribution list, and establish the cancer cell information of the corresponding patient in combination with the corresponding cancer cell actual information.

[0137] In this example, the image composition structure represents the composition of average pixel values ​​corresponding to different regions in the image domain class;

[0138] In this example, the structural domain image structural structures of the plurality of mapping regions included in the mapping background are consistent;

[0139] In this instance, the image in the class represents the image domain in the image domain class;

[0140] In this example, the feature-prominent contour represents the contour corresponding to the image in the class that can be used to express the class feature;

[0141] In this example, the analysis starting point position indicates the starting point for analyzing abnormal information;

[0142] In this instance, the normal information sample represents the sample presented by the normal information in the image domain class;

[0143] In this example, the cancer cell distribution list represents a list that counts the distribution numbers of cancer cells in different image domains.

[0144] The working principle and beneficial effects of the above technical solution are as follows: In order to further determine the basic information of cancer cells in the patient's body, the image composition of each image domain class is first determined according to the class characteristics, so as to establish a mapping background for mapping, and then the normal information and abnormal information can be found in the mapping structure to establish its abnormal information. In this way, the cancer cell distribution list and the actual information of cancer cells in the patient's body are determined, and the location and basic information of the cancer cells are further determined in detail. In addition, similar images are analyzed simultaneously during the analysis process to improve the efficiency of the analysis.

[0145] Example 7

[0146] On the basis of Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model, the abnormality level determination module includes:

[0147] A model building and analysis unit, used to build lymphatic information of a corresponding patient according to the lymphatic image, build a cell model of the corresponding patient according to the lymphatic information and the cancer cell information, and mark a cancer cell model domain in the cell model;

[0148] a cancer variable analysis unit, used to determine the cancerous organ corresponding to each cancer cell model domain, establish an organ model domain according to the normal organ data corresponding to each cancerous organ, integrate the organ model domain into the cell model, and determine the cancerous area corresponding to each cancerous organ according to the fusion result;

[0149] an organ function analysis unit, used to determine the influence parameters of cancer cells on the corresponding cancerous organ according to the cancerous area, and to adjust the corresponding organ model domain using the influence parameters to obtain the organ function characteristics of the corresponding patient;

[0150] The cancer cell information analysis unit is used to determine the cancer depth of the corresponding cancerous organ according to the organ function characteristics, and obtain the cancer volume corresponding to each image domain in combination with the corresponding cancer area.

[0151] In this example, the cancer cell model domain represents the region in the cell model that belongs to the cancer cell model, that is, the cancer cell model domain is a part of the cancer cell model.

[0152] The working principle and beneficial effects of the above technical solution are as follows: In order to further determine the degree of cancer cell pathology in a patient's body, a cell model that can express the patient's lymph information is first established based on the lymph image, and then the area of ​​the cancer cells is marked in the cell model to determine the corresponding cancerous organ, and an organ model domain is established, so that the cancerous area of ​​the cancerous organ can be known, and the corresponding cancerous depth can be determined in combination with the organ function characteristics of the organ. In this way, the amount of cancer corresponding to each image domain can be determined. In this way, the cancerous result of each organ can be accurately analyzed, allowing doctors to grasp the existing function of each organ and facilitate symptomatic treatment.

[0153] Example 8

[0154] On the basis of Example 7, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model, the abnormality level determination module further includes:

[0155] A cancer metastasis analysis unit is used to retrieve the case information of the patient, combine the cancer amount corresponding to each image domain, and obtain the cancer increase or decrease amount corresponding to each image domain;

[0156] The degree of cancer cell metastasis of the corresponding patient is generated according to the increase or decrease amount of canceration.

[0157] The working principle and beneficial effects of the above technical solution: In order to facilitate doctors to further analyze the patient's physical health status, the increase or decrease of cancer in each image domain is determined according to the patient's case information, thereby determining the degree of metastasis of cancer cells, allowing doctors to grasp the patient's physical health fluctuations in a short period of time.

[0158] Example 9

[0159] On the basis of Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model, the information sorting and display module includes:

[0160] A common marking unit, used for marking cancer cell information and cancer quantity corresponding to each image domain in the lymphatic image to generate a diagnostic image;

[0161] A lymph node screening unit, used to establish a level screening mechanism according to a plurality of preset lymph node level information, and to screen the diagnostic image using the level screening mechanism;

[0162] a lymph node grading unit, used for obtaining the lymph nodes contained in the diagnostic image and the lymph node grade corresponding to each of the lymph nodes when the diagnostic image contains lymph nodes;

[0163] The case generation unit is used to generate case information of the corresponding patient according to the cancer cell information, cancer quantity and cancer quantity and display the case information when the diagnostic image contains lymph nodes.

[0164] In this example, the lymph node grades include: grade 0 (lymph nodes are not involved, no swelling, and the texture is normal), grade 1 (lymph nodes are mildly involved, slight swelling can be felt, and there may be slight discomfort), grade 2 (lymph nodes are moderately involved, obviously swollen, hard texture, and may be accompanied by pain or tenderness), and grade 3 (lymph nodes are severely involved, with obvious swelling, hard texture, obvious pain, and may affect the function of surrounding tissues);

[0165] In this example, the marking process is as follows Figure 4 shown.

[0166] The working principle and beneficial effects of the above technical solution: In order to facilitate patients and doctors to understand the patient's physical condition, the lymph node grade corresponding to each lymph node is displayed in a concise and clear manner, and the patient's case information is established based on cancer cell information, cancerous change information and cancer variable, which improves the case and enhances the authenticity of the case.

[0167] Example 10

[0168] Based on Example 1, the pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model further includes:

[0169] The suggestion generation module is used to generate suggested treatment methods based on the case information for the doctor's reference.

[0170] The working principle and beneficial effects of the above technical solution: the doctor can make appropriate adjustments based on the recommended treatment method and then treat the patient, which reduces the efficiency of the doctor's will.

[0171] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Pan-organ lymph node metastasis cancer analysis system based on joint deep learning model, It is characterized in that include: An image segmentation and annotation module is used to obtain and perform image segmentation and image annotation on the lymphatic image, and obtain a domain image and image display response corresponding to each image domain in the lymphatic image; A domain image classification module, used to classify the image domain based on the image display response to obtain a corresponding image domain class based on a class feature corresponding to each image domain class; An abnormality analysis and positioning module, used to analyze each of the class features to determine abnormal information of the corresponding image domain class, and determine cancer cell information contained in the lymphatic image based on the abnormal information; an abnormality level determination module, used to establish a cancer number corresponding to each image domain in the lymphatic image based on the cancer cell information, and to establish a cancer cell metastasis degree of the corresponding patient based on the cancer number; The information sorting and display module is used to mark the cancer cell information and cancer variable corresponding to each image domain in the lymphatic image, generate case information of the corresponding patient and display it.

2. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 1, It is characterized in that Also includes: A metastasis analysis and estimation module, used to establish historical metastasis information and current health information of the corresponding patient according to the case information; The current health information is estimated to metastasize according to the historical metastasis information to obtain cancer metastasis estimation information of the corresponding patient, and the estimated information is transmitted to the information sorting and display module for display.

3. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 1, It is characterized in that The image segmentation and annotation module comprises: A lymphatic image capturing unit, used for scanning a patient using a specified medical imaging technology to generate a lymphatic image of the patient; A lymphatic image segmentation unit, used for performing sliding window segmentation on the lymphatic image to generate a plurality of image domains; The lymphatic image annotation unit is used to establish image comparison samples according to preset cortical features, use the image comparison samples to compare each of the image domains respectively, and establish a domain image display response corresponding to each of the image domains according to the comparison results.

4. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 3, It is characterized in that Also includes: A lesion pre-analysis module, used for obtaining cortical information of a corresponding image domain according to the domain image display response; establishing an abnormal cortical grade for the corresponding patient according to the cortical information; When the cortical abnormality level is greater than a preset level, drawing an abnormal cortical contour contained in a corresponding image domain according to the cortical information; Locating each of the abnormal cortical contours in the lymphatic image, and acquiring the immune function corresponding to each organ in the lymphatic image, to obtain the abnormal immune function corresponding to each of the abnormal cortical contours; The manifested lesion information of the corresponding patient is established according to the abnormal immune function.

5. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 1, It is characterized in that The domain image classification module comprises: An image pooling unit, used to perform image pooling processing on each of the image domains using a convolutional neural network to obtain an abstract image domain corresponding to each of the image domains; A feature generation unit, used to establish abstract description information of a corresponding image domain based on the abstract image domain, establish a pooling correction rate based on a size ratio between the abstract image domain and the corresponding image domain, and establish image description information of the corresponding image domain according to the abstract description information and the pooling correction rate; An image classification unit, used for establishing description features of a corresponding image domain according to the image description information, classifying image domains with consistent description features into one category, and generating a corresponding image domain class; A class feature establishment unit is used to establish basic features of the corresponding image domain class based on the descriptive features, establish an image sequence according to the image domain class, generate difference features between each sequence image and the basic features in the image sequence, use each of the difference features to correct the corresponding basic features, and generate class features of the corresponding image domain class.

6. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 1, It is characterized in that Abnormal analysis and positioning module, including: An image analysis mapping unit, used to establish an image composition structure of each image domain class according to the class features, establish a mapping background of the corresponding image domain class according to the image composition structure, and map the class images in the same image domain class to the corresponding mapping background to obtain a corresponding mapping result; An analysis starting point establishing unit, used to determine the characteristic prominent contour of the image in each class in the corresponding image domain class according to the class feature, locate the characteristic prominent contour in the corresponding mapping result, and establish the analysis starting point position in the mapping result according to the positioning result; an abnormal step-by-step analysis unit, used to retrieve corresponding normal information samples based on the class features, establish a search step length according to the normal information samples, and filter the mapping results for normal information and abnormal information starting from the analysis starting point to obtain normal information and abnormal information corresponding to each mapping result; The cancer cell determination unit is used to adjust the information parameters of the corresponding abnormal information according to the class characteristics, generate abnormal information of the corresponding image domain class, establish a cancer cell distribution list of the corresponding image domain class and cancer cell actual information of the corresponding image domain class according to the abnormal information, search for the position of the cancer cells contained in each of the lymphatic images according to the cancer cell distribution list, and establish the cancer cell information of the corresponding patient in combination with the corresponding cancer cell actual information.

7. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 1, It is characterized in that The abnormal level determination module includes: A model building and analysis unit, used to build lymphatic information of a corresponding patient according to the lymphatic image, build a cell model of the corresponding patient according to the lymphatic information and the cancer cell information, and mark a cancer cell model domain in the cell model; a cancer variable analysis unit, used to determine the cancerous organ corresponding to each cancer cell model domain, establish an organ model domain according to the normal organ data corresponding to each cancerous organ, integrate the organ model domain into the cell model, and determine the cancerous area corresponding to each cancerous organ according to the fusion result; an organ function analysis unit, used to determine the influence parameters of cancer cells on the corresponding cancerous organ according to the cancerous area, and to adjust the corresponding organ model domain using the influence parameters to obtain the organ function characteristics of the corresponding patient; The cancer cell information analysis unit is used to determine the cancer depth of the corresponding cancerous organ according to the organ function characteristics, and obtain the cancer volume corresponding to each image domain in combination with the corresponding cancer area.

8. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 7, It is characterized in that The abnormal level determination module further includes: A cancer metastasis analysis unit is used to retrieve the case information of the patient, combine the cancer amount corresponding to each image domain, and obtain the cancer increase or decrease amount corresponding to each image domain; The degree of cancer cell metastasis of the corresponding patient is generated according to the increase or decrease amount of canceration.

9. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model as claimed in claim 1, It is characterized in that The information sorting and display module comprises: A common marking unit, used for marking cancer cell information and cancer quantity corresponding to each image domain in the lymphatic image to generate a diagnostic image; A lymph node screening unit, used to establish a level screening mechanism according to a plurality of preset lymph node level information, and to screen the diagnostic image using the level screening mechanism; a lymph node grading unit, used for obtaining the lymph nodes contained in the diagnostic image and the lymph node grade corresponding to each of the lymph nodes when the diagnostic image contains lymph nodes; The case generation unit is used to generate case information of the corresponding patient according to the cancer cell information, cancer quantity and cancer quantity and display the case information when the diagnostic image contains lymph nodes.

10. The pan-organ lymph node metastasis cancer analysis system based on the joint deep learning model according to claim 1, It is characterized in that Also includes: The suggestion generation module is used to generate suggested treatment methods based on the case information for the doctor's reference.

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