CN120600340A - A pathology analysis system based on visual detection - Google Patents

Abstract

The invention belongs to the technical field of medical image processing and medical informatization, and discloses a pathology analysis system based on visual detection, which consists of an image acquisition and intelligent input module, an image feature extraction and index archiving module, a parameter combination generation and cancer type adaptation module, a judgment basis and evidence grade assignment module, a pathology analysis and dynamic threshold adjustment module, a result generation and multi-platform output module and a result statistics and continuous learning module. According to the invention, the pathological section images are automatically acquired through the visual detection equipment, the key indexes such as HER2 expression level, ki-67 index, FISH detection signal, cell size, arrangement mode and the like can be efficiently and accurately extracted by combining with an image feature extraction algorithm, and the extraction results are automatically archived to corresponding parameter items.

Description

Pathological analysis system based on visual detection

Technical Field

The invention belongs to the technical field of medical image processing and medical informatization, and particularly relates to a pathology analysis system based on visual detection.

Background

The existing pathological analysis flow mainly depends on manual reading of pathologists and a part of basic image recognition auxiliary system.

The manual film reading is used as a current mainstream pathological diagnosis means, obvious subjectivity difference exists, film reading results are easily influenced by artificial factors such as experience level, fatigue degree and the like, diagnosis accuracy is unstable, and structural analysis on complex parameters is difficult.

The prior auxiliary diagnosis system improves the image processing efficiency to a certain extent, but is more limited to the local feature extraction of a single image, can not carry out comprehensive combined judgment on different pathological evidence items, lacks hierarchical management on multi-parameter dynamic association and evidence level, generally adopts static judgment rules, lacks the capability of dynamic adjustment threshold and continuous optimization based on user feedback, can not adapt to complex requirements of parameter change and sample accumulation in the actual diagnosis process, and particularly can not effectively support clinical accurate medication and personalized treatment in single-parameter or single-point interpretation in pathological analysis of serious diseases such as cancers.

Disclosure of Invention

The invention aims to provide a pathology analysis system based on visual detection, so as to solve the problems in the background technology.

In order to achieve the aim, the invention provides the technical scheme that the pathology analysis system based on visual detection comprises an image acquisition and intelligent input module, an image feature extraction and index archiving module, a parameter combination generation and cancer type adaptation module, a judgment basis and evidence grade assignment module, a pathology analysis and dynamic threshold adjustment module, a result generation and multi-platform output module and a result statistics and continuous learning module;

The image acquisition and intelligent recording module acquires pathological section images through the visual detection equipment, supports the dual-channel synchronous operation of automatic retrieval recording and manual recording of photographing, realizes quick matching of the images, supports real-time complement and editing of users, and ensures high-efficiency and flexibility of recording;

The image feature extraction and index archiving module automatically extracts pathological indexes such as HER2, ki-67 and FISH, supports image measurement of parameters such as cell size, nucleolus proportion and arrangement structure, and archives the extraction result to a parameter library in real time, thereby ensuring complete, standard and accurate data;

The parameter combination generation and cancer species adaptation module automatically calls a corresponding parameter template according to the cancer type of the detected object to generate a combination containing main parameters and auxiliary parameters, supports personalized parameter adaptation of breast cancer, gastrointestinal cancer and other solid tumors, and ensures scientific and reasonable parameter combination;

the judgment basis and evidence grade assignment module is used for carrying out evidence grade division on the generated parameter combination, labeling positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence, automatically assigning confidence degrees to the parameter combinations and supporting accurate judgment;

The pathology analysis and dynamic threshold adjustment module is used for comparing the detection parameters with the judgment basis set, automatically judging positive or negative, dynamically adjusting the confidence threshold, supporting real-time optimization according to the misjudgment rate, the missed judgment rate and the total sample amount, and ensuring the scientificity and continuous adaptation of the judgment result;

The result generation and multi-platform output module is used for generating a pathology report containing detection results, parameter combinations, evidence grades, confidence and targeted drug recommendation, supporting multi-format export, batch statistics and classified display, and supporting one-key sharing to a WeChat and third-party data platform;

and the result statistics and continuous learning module is used for continuously recording detection data and user feedback, automatically comparing the prediction with the actual result, generating an error statistics chart, supporting the user to participate in optimization, dynamically adjusting parameter weights and learning periods based on multi-user data, and continuously improving judgment accuracy.

Preferably, the image acquisition and intelligent input module comprises:

(1) The image acquisition and automatic matching input comprises the steps of photographing and acquiring pathological sections through visual detection equipment, automatically extracting image features by a system, and rapidly searching and matching the image features to corresponding detection items based on a built-in pathological parameter index library, wherein the key evidence parameters comprise HER2 expression, ki-67 index, nucleolus size and cell arrangement mode; after the system completes image analysis, the matched detection result can be automatically and accurately input into a parameter list, so that high-efficiency and accurate automatic input is ensured, and real-time data support is provided for a subsequent judging module;

(2) The manual recording and the double-channel synchronous operation are carried out, in order to ensure the data integrity and the abnormal result correction, the system supports the user to carry out parameter complement or correction through the manual recording unit, the user can select or input pathological items, such as a FISH detection result, axillary lymph node metastasis and the like, support the rechecking of an automatic recording result, the automatic searching and the manual recording function can be synchronously operated, the user can also carry out manual complement at any time in the photographing automatic searching process, the system automatically and parallelly receives double-channel recording information, the recording efficiency and the accuracy are ensured to be compatible, and meanwhile, the foundation is laid for continuous learning and evidence level judgment of the system.

Preferably, the image feature extraction and index archiving module comprises:

(1) The method comprises the steps of automatically extracting image features and archiving parameters, namely automatically analyzing pathological section images based on visual detection equipment, accurately extracting key indexes including HER2 expression level, ki-67 index, FISH detection signal mean value, red-green ratio, cell size, nucleolus proportion, cell arrangement mode, cell nucleus special-shaped condition, vessel cancer embolism and nerve invasion condition through a built-in image processing algorithm, automatically archiving each extracted parameter to a system parameter entry library, ensuring ordered data structure and clear information classification, and providing comprehensive data support for subsequent judgment;

(2) The system can automatically identify cell boundaries, nucleolus forms and arrangement modes in images, quickly complete quantitative measurement of corresponding parameters, the measurement results are mapped to a system parameter entry library in real time and automatically filed to preset entry positions, the continuity and the accuracy of a parameter extraction process are ensured, and a data foundation is laid for subsequent automatic interpretation and statistical learning of pathological analysis.

Preferably, the parameter combination generates a module adapted to the cancer species:

(1) After receiving the pathological image and related information, the cancer type corresponding to the detected object, such as breast cancer, gastrointestinal cancer or other solid tumors, can be automatically identified; according to the identification result, the system automatically calls a parameter template matched with the cancer, so as to ensure that the called parameter template accords with pathological features and diagnosis requirements of the cancer;

(2) The parameter combination generation and rule matching comprises automatically selecting at least one main parameter and one auxiliary parameter according to a called cancer parameter template, generating a plurality of groups of parameter combinations according to a built-in combination rule, aiming at breast cancer, wherein the parameter combinations can comprise HER2 expression and Ki-67 index, gastrointestinal cancer can relate to MSH6 and MSH2 key indexes, other tumors call corresponding parameters, and the generated parameter combinations can be used for subsequent positive or negative judgment to ensure that the interpretation process has both universality and scientificity.

Preferably, the judging basis and evidence grade assignment module comprises:

(1) The method comprises the steps of performing automatic evidence grading on generated parameter combinations by a system, classifying the parameter combinations into four types of positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence according to clinical diagnosis rules of breast cancer and gastrointestinal cancer, wherein the expression grade of HER2 of the positive strong evidence is 3+, the detection of FISH is positive, the nucleolus is large, the Ki-67 index is higher than 70%, and the expression grade of HER2 of the positive secondary evidence is 2+, the nucleolus is large, the Ki-67 index is higher than 40%, vascular cancer embolism or nerve invasion exists;

(2) And (3) assigning probability confidence and labeling results, namely automatically assigning corresponding probability confidence to each group of divided parameter combinations based on evidence grade and historical sample statistical results, and measuring the reliability of the positive or negative results corresponding to the parameter combinations. For example, positive strong evidence corresponds to high confidence and negative strong evidence corresponds to low confidence, while the system assigns a medium confidence level to the positive secondary evidence, negative secondary evidence. All parameter combinations are in one-to-one correspondence with confidence results and are automatically marked to a system result library so as to support subsequent automatic interpretation and clinical reference.

Preferably, the pathology analysis and dynamic threshold adjustment module comprises:

(1) The parameter comparison and result automatic judgment comprises the steps of automatically comparing a parameter set of a current detection object with an established judgment basis set, and rapidly judging whether the detection result is positive or negative according to the evidence grade and the parameter combination confidence, comprehensively considering main parameters, auxiliary parameters and the evidence grade in the comparison process, ensuring strict and systematic judgment process, recording the preliminary judgment result in real time, and synchronously transmitting the preliminary judgment result to a result statistics and continuous learning module, thereby providing a data basis for the follow-up judgment optimization;

(2) The method comprises the steps of dynamically adjusting confidence thresholds, namely continuously receiving statistics data fed back by a result statistics and continuous learning module, dynamically adjusting the confidence thresholds of positive and negative according to the positive misjudgment rate and the missed judgment rate, wherein an adjustment path comprises the steps of preferentially adjusting the thresholds when the misjudgment rate fluctuates greatly, automatically optimizing a threshold updating period when the total sample amount is rapidly increased, and continuously optimizing a judgment standard by a system through dynamic adjustment, so that the overall judgment accuracy and adaptability are improved.

Preferably, the result generation and multi-platform output module includes:

(1) Generating and supporting a pathology analysis report, namely generating a complete pathology analysis report comprising positive or negative conclusions, parameter combination details, evidence grading, confidence coefficient values and targeted drug recommendation information based on an automatic judgment result, automatically matching report contents according to cancer templates such as breast cancer and gastrointestinal cancer to ensure scientific and strict data, deriving Excel, word, PDF and pictures from the report support, and facilitating long-term retention and multi-scene application of clinicians, pathology specialists and patients;

(2) The system comprises a plurality of display modes, a plurality of data transmission modes and a plurality of data transmission modes, wherein the display modes are used for supporting pathological analysis reports to be exported in single case, counted in batches and summarized and classified according to cancer types, so that requirements of clinical statistics, follow-up management and multi-case comparison are met, a one-key sharing function is supported, the reports can be rapidly transmitted to WeChat, a third-party data platform and other common applications, information circulation efficiency is improved, and the system provides flexible data output paths so that the reports can be shared among different terminals and platforms in real time and safely backed up.

Preferably, the result statistics and continuous learning module comprises:

(1) Continuously recording pathological detection parameters, prediction results and real detection results input by a user, and supporting automatic comparison of prediction conclusion and actual conditions; for the case with deviation, the system analyzes the prediction error in real time and generates an error statistics chart, so that a user can intuitively know the accuracy of the system;

(2) Parameter optimization and learning period adjustment, dynamically adjusting parameter weights, evidence priorities and positive and negative judgment thresholds based on continuously recorded data, and feeding back optimization results to a pathology analysis module and a dynamic threshold adjustment module in real time, wherein the system supports automatic triggering of parameter optimization and learning period adjustment according to the accumulated data quantity input by multiple users, ensures quick response when the sample quantity is increased, continuously improves judgment accuracy and adaptability, and simultaneously ensures transparency and rationality of a parameter adjustment process.

The beneficial effects of the invention are as follows:

1. According to the invention, the pathological section images are automatically acquired through the visual detection equipment, the key indexes such as HER2 expression level, ki-67 index, FISH detection signal, cell size, arrangement mode and the like can be efficiently and accurately extracted by combining with an image feature extraction algorithm, and the extracted results are automatically filed into corresponding parameter items.

2. The invention supports the calling of a cross-cancer type parameter template by designing a dynamic combination mechanism of main parameters and auxiliary parameters, and automatically gives the probability confidence of parameter combination based on four-level evidence classification rules of positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence defined by clients.

3. According to the invention, by supporting the user to input a real detection result and automatically comparing errors with a system prediction result, the system dynamically adjusts positive and negative judgment thresholds and optimizes parameter weights and evidence priorities by continuously recording sample data and feeding back statistical errors, so that the real-time evolution of diagnosis rules is realized; compared with the existing static diagnosis system, the system has continuous learning and self-optimizing capabilities, can adapt to parameter fluctuation, sample accumulation and user feedback change in an actual detection environment, effectively reduces misjudgment and omission judgment probability, ensures timeliness and accuracy of diagnosis results, and provides scientific and reliable technical support for long-term pathological analysis.

Drawings

FIG. 1 is a flow chart of a visual inspection-based pathology analysis system according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

As shown in fig. 1, the embodiment of the invention provides a pathology analysis system based on visual detection, which consists of an image acquisition and intelligent input module, an image feature extraction and index archiving module, a parameter combination generation and cancer seed adaptation module, a judgment basis and evidence grade assignment module, a pathology analysis and dynamic threshold adjustment module, a result generation and multi-platform output module and a result statistics and continuous learning module;

The image acquisition and intelligent recording module acquires pathological section images through the visual detection equipment, supports the dual-channel synchronous operation of automatic retrieval recording and manual recording of photographing, realizes quick matching of the images, supports real-time complement and editing of users, and ensures high-efficiency and flexibility of recording;

The embodiment of the image acquisition and intelligent input module comprises the steps of adopting visual detection equipment with a high-resolution camera to take photos of pathological sections of breast cancer in practical application, starting an automatic search program immediately after the system takes photos, automatically associating the shot sections to corresponding items such as HER2 expression, ki-67 index, FISH detection and the like in a parameter item library through quick matching of image characteristics, synchronously opening a manual input interface by the system in the shooting process, allowing a user to selectively and gradually complement parameters such as vessel cancer embolism, nerve invasion, cell nucleus abnormal shape and the like which are possibly not automatically identified, and ensuring that the user can supplement missing parameters in real time on the basis of automatic acquisition by the double-channel synchronous operation mechanism, so that the integrity and flexibility of data input are ensured.

The image feature extraction and index archiving module automatically extracts pathological indexes such as HER2, ki-67 and FISH, supports image measurement of parameters such as cell size, nucleolus proportion and arrangement structure, and archives the extraction result to a parameter library in real time, thereby ensuring complete, standard and accurate data;

The embodiment of the image feature extraction and index archiving module comprises the steps of automatically starting an image feature extraction algorithm after a pathological image is acquired by the system, taking a breast cancer slice as an example, automatically analyzing HER2 expression level by the system, extracting HER2 positive degree by comparing dyeing depth and regional distribution, simultaneously, automatically calculating kernel proportion by measuring cell diameter and kernel diameter by the system, and extracting cell arrangement modes (such as faithfulness, chordae, papillae), and synchronously extracting related indexes such as MSH6, MSH2 and the like by the system by taking a gastrointestinal cancer slice as an example. The extraction result is filed to a parameter entry library in real time, and the sample ID, the acquisition time and the detection source are automatically associated with each item of data, so that the integrity of the data structure is ensured, and the subsequent quick call is supported.

The parameter combination generation and cancer species adaptation module automatically calls a corresponding parameter template according to the cancer type of the detected object to generate a combination containing main parameters and auxiliary parameters, supports personalized parameter adaptation of breast cancer, gastrointestinal cancer and other solid tumors, and ensures scientific and reasonable parameter combination;

The parameter combination generation and cancer species adaptation module embodiment comprises the steps that when a detected object is breast cancer, a system automatically calls a breast cancer parameter template, the system at least selects one main parameter (such as HER2 expression level) and one auxiliary parameter (such as Ki-67 index) from core parameters of HER2, ki-67, nucleolus size, cell arrangement mode and the like according to the template to generate a parameter combination, for example, the system automatically forms a combination of 'HER 23++ Ki-6780%', the combination is suitable for positive strong evidence judgment, and when the detected object is switched to gastrointestinal cancer, the system automatically calls the template containing MSH6, PMS2, MLH1 and the like, and the combination rule is automatically adjusted. And combination logic is built in each cancer template, so that the pertinence and rationality of parameter combination are ensured.

The judgment basis and evidence grade assignment module is used for carrying out evidence grade division on the generated parameter combination, labeling positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence, automatically assigning confidence degrees to the parameter combinations and supporting accurate judgment;

Judging the embodiment of the evidence grade assignment module, wherein in the detection process, the system automatically matches the evidence grade rule according to the archiving parameters. Taking a breast cancer case as an example, if the HER2 expression level is detected to be 3+, the FISH detection is positive, the nucleolus is large and the Ki-67 index is higher than 70%, the system judges the group of parameters as positive strong evidence and gives high confidence (such as more than 90%), if the parameter combination shows that the HER2 expression level is 2+, the Ki-67 index is 45%, the vascular cancer plug exists, the system automatically classifies positive secondary evidence and gives medium confidence, and the system can classify and label the evidence of all the parameter combinations according to the evidence level detailed standard preset in the document so as to support the follow-up accurate judgment.

The pathology analysis and dynamic threshold adjustment module is used for comparing the detection parameters with the judgment basis set, automatically judging positive or negative, dynamically adjusting the confidence threshold, supporting real-time optimization according to the misjudgment rate, the missed judgment rate and the total sample amount, and ensuring the scientificity and continuous adaptation of the judgment result;

The pathology analysis and dynamic threshold adjustment module embodiment comprises the steps that a system compares a current parameter set of a detection object with an established positive and negative judgment basis set, for example, the current detection result is HER22+, ki-6735%, nucleolus is medium and is accompanied by a micro nipple structure, the system preliminarily judges the detection object as negative secondary evidence according to the existing judgment rule and outputs a negative result, the system synchronously retrieves historical data, the positive and negative confidence threshold values are dynamically adjusted, if the system continuously finds that the positive misjudgment rate is increased, the sensitivity of positive judgment is reduced by adjusting the threshold value, and if the total sample amount is continuously increased, the system automatically optimizes a threshold value update period to ensure that the real-time adjustment and judgment stability are compatible.

The result generation and multi-platform output module is used for generating a pathology report containing detection results, parameter combinations, evidence grades, confidence and targeted drug recommendation, supporting multi-format export, batch statistics and classified display, and supporting one-key sharing to a WeChat and third-party data platform;

The result generation and multi-platform output module embodiment comprises the steps that a system automatically generates a complete pathology analysis report, the complete pathology analysis report comprises detection conclusion (positive or negative), parameter combination details, evidence grade classification, current confidence coefficient values and recommended targeting drug lists (such as trastuzumab), report support is exported in Excel, word, PDF, pictures and other various formats, a user can share the report with a WeChat, a mailbox or a third party data platform in a one-key mode, all detection sample reports are exported in batches, the system provides various result display modes, single case detailed display is supported, batch case statistics and cancer classification summarization are supported, and the clinical and scientific research diversity requirements are met.

The result statistics and continuous learning module is used for continuously recording detection data and user feedback, automatically comparing prediction with actual results to generate an error statistics chart, supporting user participation optimization, dynamically adjusting parameter weights and learning periods based on multi-user data, and continuously improving judgment accuracy;

The embodiment of the result statistics and continuous learning module comprises that a system continuously records parameters and prediction results of each pathological detection and real detection results input by a user, the user can input actual FISH detection results after the breast cancer cases are given out by the system, the system automatically compares and generates an error statistics chart, the user can select whether current error data are included in the system for continuous learning, if so, the system automatically adjusts parameter weights, evidence priorities and judgment thresholds, and for multi-user input, the system automatically starts learning period adjustment according to accumulated sample quantity, ensures synchronization of parameter optimization rhythm and data accumulation, and continuously improves the prediction accuracy of the system.

The image acquisition and intelligent input module is used for shooting and acquiring pathological sections through visual detection equipment, automatically extracting image features by the system, quickly searching and matching the image features to corresponding detection items based on a built-in pathological parameter index library, wherein the key evidence parameters comprise HER2 expression, ki-67 indexes, nucleolus size and cell arrangement mode, after image analysis is completed, the system can automatically accurately input the matched detection results to a parameter list, ensure efficient and accurate automatic input, simultaneously provide real-time data support for a subsequent judging module, support users to carry out parameter complement or correction through a manual input unit for ensuring data integrity and abnormal result correction, enable the users to select or input pathological items item by item, such as FISH detection results, axillary lymph node metastasis and the like, support recheck the automatic input results, synchronously operate an automatic search and manual input function, enable users to carry out manual complement at any time in the automatic search process, automatically receive dual-channel information in parallel, ensure input efficiency and accuracy, and lay a foundation for continuous learning and level judgment of the system.

The image feature extraction and index archiving module is used for automatically analyzing pathological section images based on visual detection equipment, accurately extracting key indexes including HER2 expression level, ki-67 index, FISH detection signal mean value, red-green ratio, cell size, nucleolus proportion, cell arrangement mode, cell nucleus abnormal condition, vessel cancer embolism and nerve invasion condition through a built-in image processing algorithm, automatically archiving each extracted parameter to a system parameter entry library, ensuring ordered data structure and clear information classification, providing comprehensive data support for subsequent judgment, automatically measuring cell diameter, nucleolus diameter, signal intensity and cell arrangement structure through the image processing algorithm, automatically identifying cell boundary, nucleolus morphology and arrangement mode in the images by the system, rapidly completing quantitative measurement of corresponding parameters, mapping measurement results to the system parameter entry library in real time, automatically archiving to preset entry positions, ensuring continuity and accuracy of parameter extraction processes, and laying a data foundation for subsequent automatic interpretation and statistical learning of pathological analysis.

The parameter combination generation and cancer species adaptation means that after a pathological image and related information are received, cancer types corresponding to a detection object, such as breast cancer, gastrointestinal cancer or other entity tumors, can be automatically identified, a parameter template matched with the cancer species is automatically called by a system according to an identification result, the called parameter template meets pathological characteristics and diagnosis requirements of the cancer species, main parameters and auxiliary parameters are built in different cancer species parameter templates and combination rules thereof are set, pertinence and accuracy of data call are guaranteed, at least one main parameter and one auxiliary parameter are automatically selected according to the called cancer species parameter template, a plurality of groups of parameter combinations are generated according to the built-in combination rules, aiming at breast cancer, the parameter combinations can comprise HER2 expression and Ki-67 indexes, gastrointestinal cancer can relate to MSH6 and MSH2, other tumors call corresponding parameters, and the generated parameter combinations can be used for subsequent positive or negative judgment, so that the universality and scientificity of an interpretation process are ensured.

The judgment basis and evidence grade assignment module refers to that the system carries out automatic evidence grade division on the generated parameter combination, the parameter combination is divided into four types of positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence according to clinical diagnosis rules of breast cancer and gastrointestinal cancer, the expression grade of HER2 of the positive strong evidence is 3+, the detection of FISH is positive, the nucleolus is large, the Ki-67 index is higher than 70%, the expression of HER2 of the positive secondary evidence is 2+, the nucleolus is large, the Ki-67 index is higher than 40%, vascular cancer embolism or nerve invasion exists, the system rapidly classifies all the parameter combinations by matching an evidence grade library, and corresponding probability confidence is automatically given to each group of classified parameter combinations based on the evidence grade and historical sample statistical results so as to measure the reliability of the positive or negative results corresponding to the parameter combination. For example, positive strong evidence corresponds to high confidence and negative strong evidence corresponds to low confidence, while the system assigns a medium confidence level to the positive secondary evidence, negative secondary evidence. All parameter combinations are in one-to-one correspondence with confidence results and are automatically marked to a system result library so as to support subsequent automatic interpretation and clinical reference.

The pathology analysis and dynamic threshold adjustment module is used for automatically comparing a parameter set of a current detection object with an established judgment basis set, quickly judging whether a detection result is positive or negative according to an evidence grade and a parameter combination confidence coefficient, comprehensively considering main parameters, auxiliary parameters and evidence grade in a comparison process, ensuring strict judgment process and a system, recording and synchronously transmitting a preliminary judgment result in real time to a result statistics and continuous learning module, providing a data basis for subsequent judgment optimization, continuously receiving statistical data fed back by the result statistics and continuous learning module, dynamically adjusting confidence coefficient thresholds of the positive and negative according to a positive misjudgment rate and a missed judgment rate, adjusting a threshold when the misjudgment rate fluctuates greatly, automatically optimizing a threshold updating period when the total sample amount rapidly increases, and continuously optimizing a judgment standard by dynamic adjustment, thereby improving the overall judgment accuracy and adaptability.

The result generation and multi-platform output module is used for generating a complete pathology analysis report containing positive or negative conclusion, parameter combination details, evidence grading, confidence value and targeted drug recommendation information based on automatic judgment results, automatically matching report contents according to cancer templates such as breast cancer and gastrointestinal cancer to ensure scientific and strict data, exporting the report support to Excel, word, PDF and pictures to facilitate long-term retention and multi-scenario application of clinicians, pathology specialists and patients, supporting the pathology analysis report to be exported in single case, batch statistics and summarization and classified according to cancer types to meet the requirements of clinical statistics, follow-up management and multi-case comparison, supporting a one-key sharing function, enabling the report to be rapidly sent to a WeChat, a third-party data platform and other common applications to improve information circulation efficiency, and providing a flexible data output path to facilitate real-time sharing and safe backup of the report between different terminals and platforms.

The result statistics and continuous learning module is used for continuously recording pathological detection parameters, prediction results and real detection results input by a user and supporting automatic comparison of prediction conclusion and actual conditions; for the case with deviation, the system analyzes the prediction error in real time and generates an error statistics chart, so that a user can intuitively know the accuracy of the system, the user can autonomously select whether error data is used for subsequent parameter optimization, the system synchronously supports batch data import and statistics, the continuous and effective data accumulation process is ensured, the parameter weight, the evidence priority and the positive and negative judgment threshold value are dynamically adjusted based on the continuously recorded data, an optimization result is fed back to a pathology analysis module and a dynamic threshold value adjustment module in real time, the system supports automatic triggering of parameter optimization and learning period adjustment according to the accumulated data quantity input by multiple users, the rapid response is ensured when the sample quantity is increased, the judgment accuracy and adaptability are continuously improved, and meanwhile, the transparency and rationality of the parameter adjustment process are ensured.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. A pathology analysis system based on visual detection is characterized by comprising an image acquisition and intelligent input module, an image feature extraction and index archiving module, a parameter combination generation and cancer seed adaptation module, a judgment basis and evidence grade assignment module, a pathology analysis and dynamic threshold adjustment module, a result generation and multi-platform output module and a result statistics and continuous learning module;

The image acquisition and intelligent recording module acquires pathological section images through the visual detection equipment, supports the dual-channel synchronous operation of automatic retrieval recording and manual recording of photographing, realizes quick matching of the images, supports real-time complement and editing of users, and ensures high-efficiency and flexibility of recording;

The image feature extraction and index archiving module automatically extracts pathological indexes such as HER2, ki-67 and FISH, supports image measurement of parameters such as cell size, nucleolus proportion and arrangement structure, and archives the extraction result to a parameter library in real time, thereby ensuring complete, standard and accurate data;

The parameter combination generation and cancer species adaptation module automatically calls a corresponding parameter template according to the cancer type of the detected object to generate a combination containing main parameters and auxiliary parameters, supports personalized parameter adaptation of breast cancer, gastrointestinal cancer and other solid tumors, and ensures scientific and reasonable parameter combination;

the judgment basis and evidence grade assignment module is used for carrying out evidence grade division on the generated parameter combination, labeling positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence, automatically assigning confidence degrees to the parameter combinations and supporting accurate judgment;

The pathology analysis and dynamic threshold adjustment module is used for comparing the detection parameters with the judgment basis set, automatically judging positive or negative, dynamically adjusting the confidence threshold, supporting real-time optimization according to the misjudgment rate, the missed judgment rate and the total sample amount, and ensuring the scientificity and continuous adaptation of the judgment result;

The result generation and multi-platform output module is used for generating a pathology report containing detection results, parameter combinations, evidence grades, confidence and targeted drug recommendation, supporting multi-format export, batch statistics and classified display, and supporting one-key sharing to a WeChat and third-party data platform;

and the result statistics and continuous learning module is used for continuously recording detection data and user feedback, automatically comparing the prediction with the actual result, generating an error statistics chart, supporting the user to participate in optimization, dynamically adjusting parameter weights and learning periods based on multi-user data, and continuously improving judgment accuracy.

2. The pathology analysis system according to claim 1, wherein the image acquisition and intelligent input module comprises:

(1) The image acquisition and automatic matching input comprises the steps of photographing and acquiring pathological sections through visual detection equipment, automatically extracting image characteristics by a system, quickly searching and matching the image characteristics to corresponding detection items based on a built-in pathological parameter index library, wherein the key evidence parameters comprise HER2 expression, ki-67 index, nucleolus size and cell arrangement mode;

(2) The manual input and the double-channel synchronous operation are carried out, in order to ensure the data integrity and the abnormal result correction, the system supports the user to carry out parameter complement or correction through the manual input unit, the user can select or input pathological items item by item and support the rechecking of the automatic input result, the automatic search and the manual input function can be carried out synchronously, the user can also carry out manual complement at any time in the automatic shooting search process, the system automatically receives double-channel input information in parallel, and the input efficiency and the accuracy are ensured.

3. The pathology analysis system according to claim 1, wherein the image feature extraction and index archiving module comprises:

(1) The method comprises the steps of automatically extracting image features and archiving parameters, namely automatically analyzing pathological section images based on visual detection equipment, and accurately extracting key indexes including HER2 expression level, ki-67 index, FISH detection signal mean value, red-green ratio, cell size, nucleolus proportion, cell arrangement mode, cell nucleus abnormal condition, vessel cancer embolism and nerve invasion condition through a built-in image processing algorithm;

(2) The automatic measurement and structural parameter mapping comprises the steps of automatically measuring cell diameter, nucleolus diameter, signal intensity and cell arrangement structure through an image processing algorithm, automatically identifying cell boundaries, nucleolus forms and arrangement modes in images by a system, rapidly completing quantitative measurement of corresponding parameters, mapping measurement results to a system parameter entry library in real time, automatically archiving the measurement results to preset entry positions, and guaranteeing continuity and accuracy of a parameter extraction process.

4. The pathology analysis system according to claim 1, wherein the parameter combination generation and cancer adaptation module:

(1) The cancer type identification and parameter template call can automatically identify the cancer type corresponding to the detected object after receiving the pathological image and related information; according to the identification result, the system automatically calls a parameter template matched with the cancer, so as to ensure that the called parameter template accords with pathological features and diagnosis requirements of the cancer;

(2) The parameter combination generation and rule matching comprises automatically selecting at least one main parameter and one auxiliary parameter according to a called cancer parameter template, generating a plurality of groups of parameter combinations according to a built-in combination rule, aiming at breast cancer, wherein the parameter combinations can comprise HER2 expression and Ki-67 index, gastrointestinal cancer can relate to MSH6 and MSH2 key indexes, other tumors call corresponding parameters, and the generated parameter combinations can be used for subsequent positive or negative judgment to ensure that the interpretation process has both universality and scientificity.

5. The pathology analysis system according to claim 1, wherein the judgment basis and evidence grade assignment module comprises:

(1) The method comprises the steps of performing automatic evidence grading on generated parameter combinations by a system, classifying the parameter combinations into four types of positive strong evidence, positive secondary evidence, negative strong evidence and negative secondary evidence according to clinical diagnosis rules of breast cancer and gastrointestinal cancer, wherein the expression grade of HER2 of the positive strong evidence is 3+, the detection of FISH is positive, the nucleolus is large, the Ki-67 index is higher than 70%, and the expression grade of HER2 of the positive secondary evidence is 2+, the nucleolus is large, the Ki-67 index is higher than 40%, vascular cancer embolism or nerve invasion exists;

(2) The probability confidence assignment and the result labeling are that corresponding probability confidence is automatically given to each group of divided parameter combinations based on evidence grade and historical sample statistical results, the probability confidence is used for measuring the reliability degree of positive or negative results corresponding to the parameter combinations, all the parameter combinations are in one-to-one correspondence with the confidence results, and the confidence results are automatically labeled to a system result library to support subsequent automatic interpretation and clinical reference.

6. The pathology analysis system according to claim 1, wherein the pathology analysis and dynamic threshold adjustment module comprises:

(1) Automatically comparing the parameter set of the current detection object with an established judgment basis set, and rapidly judging whether the detection result is positive or negative according to the evidence grade and the parameter combination confidence, comprehensively considering main parameters, auxiliary parameters and the evidence grade in the comparison process, ensuring strict and systematic judgment process, recording the preliminary judgment result in real time, and synchronously transmitting the preliminary judgment result to a result statistics and continuous learning module;

(2) The method comprises the steps of dynamically adjusting confidence thresholds, namely continuously receiving statistics data fed back by a result statistics and continuous learning module, dynamically adjusting the confidence thresholds of positive and negative according to the positive misjudgment rate and the missed judgment rate, wherein an adjustment path comprises the steps of preferentially adjusting the thresholds when the misjudgment rate fluctuates greatly, automatically optimizing a threshold updating period when the total sample amount is rapidly increased, and continuously optimizing a judgment standard by a system through dynamic adjustment, so that the overall judgment accuracy and adaptability are improved.

7. The pathology analysis system according to claim 1, wherein the result generation and multi-platform output module comprises:

(1) Generating and supporting a pathology analysis report, namely generating a complete pathology analysis report comprising positive or negative conclusions, parameter combination details, evidence grading, confidence coefficient values and targeted drug recommendation information based on an automatic judgment result, automatically matching report contents according to cancer templates such as breast cancer and gastrointestinal cancer to ensure scientific and strict data, deriving Excel, word, PDF and pictures from the report support, and facilitating long-term retention and multi-scene application of clinicians, pathology specialists and patients;

(2) The method has the advantages of supporting the multi-display modes of single case export, batch statistics and summarization and cancer classification of pathological analysis reports, meeting the requirements of clinical statistics, follow-up management and multi-case comparison, supporting one-key sharing, enabling the reports to be rapidly sent to WeChat, third-party data platforms and other common applications, and providing flexible data output paths.

8. The pathology analysis system according to claim 1, wherein the result statistics and continuous learning module comprises:

(1) Continuously recording pathological detection parameters, prediction results and real detection results input by a user, and supporting automatic comparison of prediction conclusion and actual conditions; for the case with deviation, the system analyzes the prediction error in real time and generates an error statistics chart, so that a user can intuitively know the accuracy of the system;

(2) Parameter optimization and learning period adjustment, dynamically adjusting parameter weights, evidence priorities and positive and negative judgment thresholds based on continuously recorded data, and feeding back optimization results to a pathology analysis module and a dynamic threshold adjustment module in real time, wherein the system supports automatic triggering of parameter optimization and learning period adjustment according to the accumulated data quantity input by multiple users, ensures quick response when the sample quantity is increased, continuously improves judgment accuracy and adaptability, and simultaneously ensures transparency and rationality of a parameter adjustment process.