Detection system for assisting accurate positioning of chest lesion

Abstract

The invention relates to the field of medical instruments, in particular to a detection system for assisting accurate positioning of chest lesions, which comprises a data acquisition module for acquiring preoperative CT image data; the three-dimensional reconstruction module is used for converting into a three-dimensional model; the coordinate construction module is used for establishing a unified virtual coordinate system and determining key positioning points; the virtual cutting module is used for simulating different paths and angles to perform pathological section on the three-dimensional model; the image tracking module is used for capturing image data in the body of the patient in real time during an operation and fusing the image data with the three-dimensional model based on the key positioning points; the image processing module is used for carrying out image acquisition and processing on the pathological section; and the space verification module is used for carrying out space position matching on the image data and the three-dimensional model and calculating the similarity between the image and the corresponding region and the overlap ratio index of the lesion position. The objective of the invention is to realize accurate spatial position correspondence between the lesion area in the preoperative CT image and the actual resection pathological section in the operation.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular to a detection system for assisting in the precise positioning of chest lesions. Background Art

[0002] In the surgical treatment of chest diseases, ensuring accurate positioning of the lesion area is one of the key factors for the success of the operation. Due to the complexity of the chest anatomy, including multiple important organs, blood vessels and nerves, accurate positioning of the lesion area is crucial to reducing surgical risks, protecting normal tissues and improving treatment effects.

[0003] Traditional surgical methods mainly rely on the doctor's clinical experience, direct observation during surgery, and touch to determine the location of the lesion. Although this method can meet surgical needs to a certain extent, it has significant limitations and shortcomings. First, the doctor's experience and subjective judgment may lead to positioning errors, especially for lesions with blurred boundaries or deep lesions. Second, direct observation during surgery is affected by the limitation of the visual field and the obstruction of anatomical structures, making it difficult to fully and accurately assess the scope of the lesion and its relationship with surrounding tissues.

[0004] With the rapid development of medical imaging technology, especially the widespread application of computed tomography (CT) technology, it has become possible to accurately reconstruct and locate the lesion area before surgery. CT images provide doctors with rich lesion information with their high resolution and clear anatomical structure display capabilities. However, although preoperative CT images can provide detailed lesion location and morphological information, how to accurately match this information to the actual resection position during surgery remains a technical challenge.

[0005] Therefore, developing a detection system that can efficiently and accurately achieve the precise positioning of auxiliary chest lesions in preoperative CT images corresponding to the spatial position of pathological slices actually removed during surgery is of great significance for improving the success rate and safety of surgical treatment of chest diseases. Summary of the invention

[0006] To solve the above problems, the present invention provides a detection system for assisting in the precise positioning of chest lesions, which aims to achieve precise spatial position correspondence between the lesion area in the preoperative CT image and the actual resected pathological slice during surgery by integrating advanced medical image processing, three-dimensional reconstruction, intraoperative image tracking and spatial position matching technology, thereby improving the accuracy and safety of surgical treatment of chest diseases.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows: A detection system for assisting the precise positioning of chest lesions, comprising:

[0008] A data acquisition module, used to acquire preoperative CT image data;

[0009] A three-dimensional reconstruction module, used for converting the image data collected by the data acquisition module into a three-dimensional model;

[0010] A coordinate construction module is used to establish a unified virtual coordinate system in the three-dimensional model and to establish several key positioning points in the virtual coordinate system;

[0011] Virtual cutting module, used to simulate different paths and angles to perform pathological sections on 3D models;

[0012] Image tracking module, used to capture real-time image data inside the patient's body during surgery and fuse the image data with the 3D model based on key positioning points;

[0013] An image processing module, used for collecting and processing images of pathological sections made using the paths and angles simulated in the virtual cutting module;

[0014] The spatial verification module is used to match the image data processed by the image processing module with the three-dimensional model in spatial position, and to verify whether the spatial correspondence between the preoperative plan and the actual surgical result is accurate by calculating the similarity between the slice image and the corresponding area of ​​the three-dimensional model and the coincidence index of the lesion position.

[0015] The technical principle of the above scheme is as follows: Use high-precision CT scanning equipment to obtain preoperative imaging data of the patient's chest. Subsequently, these two-dimensional imaging data are converted into a three-dimensional model through a three-dimensional reconstruction algorithm to restore the three-dimensional structure of the patient's chest, providing a basis for subsequent surgical planning. Construct a unified virtual coordinate system in the three-dimensional model, which is used to standardize and locate various anatomical structures and lesion areas in the chest. Through expert knowledge or automatic algorithms, establish several key positioning points in the coordinate system. These points are usually important anatomical landmarks or significant feature points of the lesion boundary. Use the virtual cutting module to simulate different paths and angles to perform pathological sections on the three-dimensional model.

[0016] During the operation, the image tracking module is used to capture the image data in the patient's body in real time, and based on the key positioning points established previously, these real-time images are accurately integrated with the three-dimensional model constructed before the operation. This process ensures that the doctor can obtain the accurate location information of the lesions in the patient's body in real time during the operation. The pathological slices simulated by the virtual cutting module are imaged and processed to extract the characteristic information of the lesion area. Then, the spatial verification module is used to match the processed image data with the three-dimensional model in spatial position. By calculating the similarity between the slice image and the corresponding area of ​​the three-dimensional model and the overlap index of the lesion position, the accuracy of the spatial correspondence between the preoperative plan and the actual surgical results is verified.

[0017] The above scheme has the following beneficial effects:

[0018] 1. In this solution, the spatial verification module provides a quantitative evaluation of the spatial correspondence between preoperative planning and actual intraoperative execution by calculating the similarity between the corresponding areas of the pathological slice image and the 3D model and the coincidence index of the lesion position. This can not only verify the accuracy of preoperative planning, but also provide valuable data support during postoperative analysis, helping doctors to continuously optimize surgical plans and improve the overall treatment level.

[0019] 2. This solution, through data acquisition, 3D reconstruction and coordinate construction modules, can generate a highly accurate 3D model of the patient's chest and establish key positioning points in the model. This greatly improves the accuracy of surgical planning, allowing surgeons to clearly understand the location of the lesion and its spatial relationship with surrounding tissues before surgery, thereby achieving more precise operations during surgery and reducing damage to normal tissues.

[0020] 3. This solution realizes seamless connection between images and entities: The collaborative work of the image tracking module and the image processing module enables the real-time image data captured during surgery to be quickly integrated with the 3D model constructed before surgery. This real-time image fusion technology provides surgeons with instant feedback on the location of the lesion during surgery, which helps to adjust the surgical strategy in a timely manner and ensure the accuracy of the surgical path and resection range.

[0021] 4. This solution, through intuitive 3D models and image fusion technology, allows doctors to explain the condition, surgical plan and expected results to patients and their families more clearly, enhancing communication and trust between doctors and patients. At the same time, this high-precision preoperative planning and intraoperative guidance also enhances patients' confidence in the success of the operation and is conducive to postoperative recovery.

[0022] Furthermore, the data collection module is also used to collect patient information, medical history records and drug allergy history.

[0023] Beneficial effects: By comprehensively collecting patients' personal information, detailed medical history, and drug allergy history, doctors can more accurately assess patients' health status and surgical risks, thereby developing more personalized treatment plans. This helps avoid potential complications and improves the safety and success rate of surgery. The collection of drug allergy history is crucial to ensure patient safety. Before surgery, doctors can learn in detail which drugs patients are allergic to, thereby avoiding the use of drugs that may cause adverse reactions during surgery. This helps reduce the occurrence of medical accidents and improves the quality and safety of medical care.

[0024] Furthermore, the specific steps of the key positioning point establishment process are as follows:

[0025] A1: Based on the anatomical features of the 3D model, the image processing technology is used to automatically identify and mark the boundaries and center points of the anatomical landmarks as the first marking points;

[0026] A2: According to the needs of surgical planning, combined with the CT impact data collected in the data acquisition module, several representative points are selected from the first marking points and recorded as the second marking points;

[0027] A3: Based on the simulated cutting path and angle in the virtual cutting module, verify and adjust the position of the second marking point, and record the verification and adjustment results as key positioning points.

[0028] Beneficial effects: Through the automatic recognition and marking of image processing technology in step A1, the accuracy of the boundaries and center points of anatomical landmarks is ensured, providing a solid foundation for subsequent positioning. In step A2, representative points are selected according to the needs of surgical planning, and the positioning points are further refined to make them closer to the actual needs of the surgery. The simulated cutting path and angle verification in step A3 ensure the feasibility and accuracy of key positioning points in actual surgery and improve the accuracy of the surgery. The establishment of key positioning points is an important part of surgical planning. Accurate positioning points help doctors clarify the surgical path, resection range and possible anatomical obstacles before surgery, so as to develop a more reasonable and scientific surgical plan.

[0029] Further, anatomical landmarks in A1 include, but are not limited to, ribs, vertebrae, major blood vessels, and lung lobe boundaries.

[0030] Beneficial effects: Clarifying the types of anatomical landmarks allows image processing technology to focus on these key areas more accurately during automatic recognition. As hard tissues, the boundaries of ribs and vertebrae are relatively clear and easy to identify; while major blood vessels and lung lobe boundaries provide important soft tissue information, which is crucial for surgical path planning and risk assessment. Accurately marking these anatomical landmarks in the 3D model can provide a more detailed anatomical reference for preoperative planning. During surgical navigation, doctors can compare the actual surgical images with the anatomical landmarks in the 3D model in real time to ensure the accuracy of the surgical path and resection range.

[0031] Furthermore, the first marking point in A1 includes, but is not limited to: the center of the lesion, a specific position of the edge of the lesion, an intersection of important anatomical structures adjacent to the lesion, and an obstacle point adjacent to the lesion.

[0032] Beneficial effects: By identifying the lesion center, specific edge locations, and the intersection of adjacent important anatomical structures as the first marking point, doctors can plan the surgical path and resection range more accurately. This helps ensure that the surgical operation is directly targeted at the lesion area while reducing damage to surrounding normal tissues. Including obstacle points (such as important blood vessels, nerves, etc.) in the scope of the first marking point helps doctors identify and avoid these potential danger areas before surgery. This greatly reduces the risk of complications caused by accidental injury to important tissues during surgery and improves the safety of surgery.

[0033] Furthermore, the spatial position matching process in the spatial verification module is:

[0034] B1: Through virtual coordinates, the two-dimensional image of the pathological slice is mapped to the corresponding plane of the three-dimensional model constructed in the three-dimensional reconstruction module according to the transformation matrix obtained by registration;

[0035] B2: Using key positioning points, check whether the anatomical features in the slice image are consistent with the key positioning points of the corresponding area in the 3D model, and adjust the correspondence between the pathological slice and the 3D model;

[0036] B3: For the slice image mapped onto the 3D model, calculate the similarity between it and the corresponding area of ​​the 3D model;

[0037] B4: Based on the confirmation of the similarity between the corresponding areas of the slice image and the 3D model, the coincidence of the lesion position in the slice image and the 3D model is evaluated;

[0038] B5: Based on the results of similarity calculation and lesion position coincidence evaluation, spatial position matching verification is performed.

[0039] Beneficial effects: The spatial position matching process (B1 to B5) in the spatial verification module is designed to ensure the precise correspondence between the pathological slice image and the three-dimensional reconstructed model, thereby verifying the spatial consistency of the spatial position matching. By mapping the two-dimensional image of the pathological slice to the corresponding plane of the three-dimensional model and checking the consistency of the anatomical features, and by showing the correspondence between the pathological slice and the three-dimensional model, doctors can more intuitively explain the surgical process and results to patients and their families, and enhance communication and understanding between doctors and patients. After surgery, doctors can evaluate the success of the operation and identify possible problems by comparing the spatial correspondence between the preoperative plan and the actual surgical results. This provides valuable experience and improvement directions for future surgical operations.

[0040] Further, the similarity calculation process in B3 is: based on the similarity measurement method of image features, the grayscale distribution, texture features or structural information of the corresponding area of ​​the pathological slice image and the three-dimensional model are compared to calculate the similarity score between the two;

[0041] The similarity measurement methods include but are not limited to: mutual information, normalized cross-correlation or structural similarity index.

[0042] Beneficial effects: By comparing image features such as grayscale distribution, texture features or structural information, the similarity between the pathological slice image and the corresponding area of ​​the 3D model can be more comprehensively evaluated. These features have unique manifestations in different types of lesions and anatomical structures, so they can provide more accurate matching results. Similarity measurement methods such as mutual information, normalized cross-correlation and structural similarity index each have different characteristics and advantages. The strategy of combining multiple measurement methods can make up for the shortcomings of a single method and improve the adaptability and robustness of the algorithm in different situations.

[0043] Furthermore, the calculation process of the coincidence degree in B4 is as follows: marking the boundaries of the lesion location in the corresponding areas of the pathological slice image and the three-dimensional model respectively;

[0044] The spatial geometry algorithm is used to calculate the ratio of the overlapping area or volume between the two boundaries to the total area or volume of the lesion, which is the coincidence degree of the lesion location.

[0045] Beneficial effects: The calculation of coincidence provides a quantitative indicator for the evaluation of surgical results. Doctors can intuitively understand the degree of deviation between the actual surgical results and the preoperative plan, so as to more accurately evaluate the success of the operation. By comparing the coincidence of the pathological slice image with the lesion position in the 3D model, doctors can promptly discover possible deviations during the operation and make adjustments in subsequent operations to improve the accuracy of the operation. The evaluation results of coincidence can provide a valuable reference for future surgical planning. Doctors can continuously optimize the surgical path and resection range based on the coincidence data of historical operations to improve the success rate and safety of the operation.

[0046] Furthermore, it also includes a user interaction module, which is used to display the three-dimensional model, virtual cutting path, intraoperative image and space verification results, and provide an operation interface for the user to input instructions and parameters.

[0047] Beneficial effects: The user interaction module enables doctors to intuitively understand the patient's anatomical structure and pathological conditions through the visualization of the three-dimensional model. This helps doctors to more comprehensively evaluate the condition before surgery and develop a more scientific and reasonable surgical plan. At the same time, the real-time display of intraoperative images allows doctors to understand the progress of the operation in a timely manner and adjust the surgical strategy according to the actual situation. The demonstration function of the virtual cutting path allows doctors to simulate and optimize the surgical path before surgery, thereby reducing surgical risks and improving the accuracy of surgical operations. Doctors can practice repeatedly in a virtual environment to find the best surgical path and resection range to ensure that every step of the surgical process is accurate. The user interaction module provides an intuitive and vivid platform for communication between doctors and patients. Doctors can explain the surgical process and expected results in detail to patients and their families by showing three-dimensional models, virtual cutting paths and intraoperative images, thereby enhancing their trust and confidence in treatment. At the same time, patients can also learn about the relevant information of the surgery through the operation interface, raise their own questions and suggestions, and promote communication and understanding between doctors and patients.

[0048] Furthermore, it also includes a data management module for automatically recording and storing all acquired imaging data, processing results, preoperative planning information, and real-time intraoperative data, and also integrates a data export function for exporting key data into a common format.

[0049] Beneficial effects: The data management module ensures that all key data generated during the operation can be properly preserved through automated data recording and storage processes. This avoids data loss due to human negligence or system failure, and ensures the security and integrity of the data. Centrally managed data resources enable doctors and other medical workers to access and query required information at any time, thereby improving the efficiency of data utilization. Doctors can make more accurate decisions based on historical and real-time data to optimize surgical plans and execution processes. The data export function integrated in the data management module allows key data to be easily exported into a common format, which is convenient for sharing and communication between different systems and institutions. This helps to promote the optimal allocation and collaboration of medical resources and improve the efficiency and service level of the entire medical industry.

[0050] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A framework diagram of an embodiment of a detection system for assisting in accurate positioning of chest lesions according to the present invention;

[0052] Figure 2 A flow chart of establishing key positioning points in an embodiment of a detection system for assisting in accurate positioning of chest lesions according to the present invention;

[0053] Figure 3 This is a flow chart of the spatial position matching process in an embodiment of the detection system for assisting in the precise positioning of chest lesions of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The following is further described in detail through specific implementation methods:

[0056] Embodiment 1:

[0057] As attached Figures 1 to 3 Shown: A detection system for assisting in the precise positioning of chest lesions, comprising:

[0058] The data acquisition module is used to collect preoperative CT image data of the patient's chest through high-precision CT scanning equipment; it is also used to collect patient information (such as name, age and gender, etc.), medical history records (including previous diseases and treatment processes, etc.) and drug allergy history.

[0059] The three-dimensional reconstruction module is used to convert the two-dimensional CT image data collected by the data acquisition module into a three-dimensional model. The three-dimensional model can accurately reflect the actual situation of the patient's chest and provide a reliable reference for surgery.

[0060] The coordinate construction module is used to establish a unified virtual coordinate system in the three-dimensional model and establish several key positioning points in the virtual coordinate system; the specific steps of the key positioning point establishment process are as follows:

[0061] A1: Based on the anatomical features of the three-dimensional model, image processing technology is used to automatically identify and mark the boundaries and center points of anatomical landmarks, where the anatomical landmarks include but are not limited to: ribs, vertebrae, major blood vessels and lung lobe boundaries. The above boundaries and center points are used as preliminary first marking points, which include but are not limited to: the center of the lesion, the specific position of the edge of the lesion, the intersection of important anatomical structures adjacent to the lesion, and obstacle points adjacent to the lesion.

[0062] A2: According to the needs of surgical planning, such as surgical approach, resection range, etc., combined with the CT impact data collected in the data acquisition module, several representative points are selected from the first marking points and recorded as second marking points; these second marking points should be able to comprehensively reflect the key anatomical features and lesion conditions of the surgical area.

[0063] A3: Based on the simulated cutting path and angle in the virtual cutting module, verify and adjust the position of the second marking point, and record the verification and adjustment results as key positioning points.

[0064] The virtual cutting module is used to simulate different paths and angles for pathological sectioning of 3D models. During the simulation, doctors can freely adjust the cutting path and angle to observe the effects of different cutting schemes on the lesion area and its surrounding important anatomical structures. The virtual cutting module also has high-precision image rendering and computing capabilities, and can generate realistic pathological section images, providing doctors with intuitive visual references.

[0065] The image tracking module is used to capture real-time imaging data (such as X-rays, CT scans, etc.) inside the patient's body during surgery, and to fuse the imaging data with the three-dimensional model based on key positioning points. Through image tracking, doctors can observe changes in the surgical site in real time to ensure consistency between surgical operations and preoperative planning.

[0066] The image processing module is used to collect and process images of pathological sections using the paths and angles simulated in the virtual cutting module. By comprehensively using a variety of image processing technologies, the scanning and image processing of actual pathological sections can provide doctors with high-quality and high-precision medical image analysis services.

[0067] The spatial verification module is used to match the image data processed by the image processing module with the three-dimensional model in spatial position, and to verify whether the spatial correspondence between the preoperative plan and the actual surgical result is accurate by calculating the similarity between the slice image and the corresponding area of ​​the three-dimensional model and the coincidence index of the lesion position. The spatial position matching process in the spatial verification module is as follows:

[0068] B1: Through virtual coordinates, the two-dimensional image of the pathological slice is mapped to the corresponding plane of the three-dimensional model constructed in the three-dimensional reconstruction module according to the transformation matrix obtained by the registration.

[0069] B2: Use key positioning points to check whether the anatomical features in the slice image are consistent with the key positioning points of the corresponding area in the 3D model, and adjust the correspondence between the pathological slice and the 3D model.

[0070] B3: For the slice image mapped onto the three-dimensional model, the similarity between the slice image and the corresponding area of ​​the three-dimensional model is calculated; the similarity calculation process is: based on the similarity measurement method of image features, the grayscale distribution, texture features or structural information of the pathological slice image and the corresponding area of ​​the three-dimensional model are compared, and the similarity score between the two is calculated; wherein the similarity measurement method includes but is not limited to: mutual information, normalized cross-correlation or structural similarity index.

[0071] B4: On the basis of confirming the similarity between the corresponding areas of the slice image and the three-dimensional model, evaluate the overlap of the lesion position in the slice image and the three-dimensional model; the overlap calculation process is as follows: mark the boundaries of the lesion position in the corresponding areas of the pathological slice image and the three-dimensional model respectively; use the spatial geometry algorithm to calculate the ratio of the overlapping area or volume between the above two boundaries to the total area or volume of the lesion, which is the overlap of the lesion position.

[0072] B5: Based on the results of similarity calculation and lesion position coincidence evaluation, spatial position matching verification is performed.

[0073] It also includes a user interaction module, which is used to display the three-dimensional model, virtual cutting path, intraoperative images and spatial verification results, and provide an operation interface for users to input instructions and parameters.

[0074] The specific implementation process is as follows: Through the data acquisition module, enter the patient's name, age, gender, medical history, and history of drug allergies and other basic information. Use high-precision CT scanning equipment to perform a preoperative CT scan of the patient's chest to obtain high-quality two-dimensional CT image data. Use the three-dimensional reconstruction module to convert the collected two-dimensional CT image data into a three-dimensional model. This model must accurately reflect the anatomical structure of the patient's chest, including ribs, vertebrae, major blood vessels, and lung lobes. Through the virtual cutting module, simulate different paths and angles to perform pathological sections on the three-dimensional model. Doctors can freely adjust the cutting plan and observe its impact on the lesion area and surrounding important anatomical structures. Use high-precision image rendering and computing power to generate realistic pathological section images to provide doctors with intuitive visual references.

[0075] Based on the anatomical features of the 3D model, image processing technology is used to automatically identify and mark the boundaries and center points of anatomical landmarks as the initial first marking points. According to the surgical planning requirements, several representative points are selected from the first marking points as the second marking points to ensure that these points can fully reflect the key anatomical features and pathological conditions of the surgical area. The simulated cutting path and angle in the virtual cutting module are used to verify and adjust the position of the second marking point, and finally established as the key positioning point.

[0076] During the operation, the image tracking module is used to capture the image data (such as X-ray, CT, etc.) in the patient's body in real time. Based on key positioning points, the intraoperative image data is fused with the 3D model to ensure the consistency of the surgical operation with the preoperative plan.

[0077] The image processing module is used to collect and process the pathological slices under the paths and angles simulated in the virtual cutting module, providing high-quality and high-precision medical image analysis services. The spatial verification module is used to match the processed image data with the three-dimensional model in spatial position, and the similarity between the slice image and the corresponding area of ​​the three-dimensional model and the overlap of the lesion position are calculated. The two-dimensional image of the pathological slice is mapped to the corresponding plane of the three-dimensional model, and the corresponding relationship is adjusted to ensure consistency.

[0078] Based on the image feature similarity measurement method, the similarity between the slice image and the corresponding area of ​​the 3D model is calculated; at the same time, the overlap of the lesion position in the two is evaluated. Based on the calculation results of the similarity and overlap, the accuracy of the spatial correspondence between the preoperative planning and the actual surgical results is analyzed.

[0079] The user interaction module displays the 3D model, virtual cutting path, intraoperative images and spatial verification results. An operation interface is provided for doctors to input instructions and parameters to interact with the system.

[0080] Embodiment 2:

[0081] The difference from Example 1 is that it also includes a data management module for automatically recording and storing all acquired image data, processing results, preoperative planning information and real-time intraoperative data, and also integrates a data export function for exporting key data into a common format.

[0082] The specific implementation process is as follows: The data management module is a vital part of the medical image processing and surgical assistance system. It is responsible for efficiently and safely managing the entire process of data from data acquisition to processing and analysis, to preoperative planning and real-time application during surgery.

[0083] Medical imaging equipment (such as CT) is used to collect patient imaging data, intraoperative real-time data (such as location information of the surgical navigation system, vital sign monitoring data, etc.), and patient information, and pre-processing operations such as denoising, enhancement, and segmentation are performed to improve the accuracy of subsequent processing and analysis. The processing results are stored in a database in an appropriate format (such as images, reports, statistical charts) for subsequent review and analysis. Ensure that intraoperative real-time data can be synchronized to the data management system in real time so that doctors can view and adjust treatment strategies at any time during the operation.

[0084] Provides data export function to convert key data (such as treatment results, diagnosis reports, preoperative planning plans, etc.) into common formats such as PDF, Excel, CSV, etc. Supports cross-platform and cross-system data sharing, facilitating information exchange and collaboration among doctors, patients, research institutions, and other parties.

[0085] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A detection system for assisting the precise positioning of chest lesions, characterized in that: include: A data acquisition module, used to acquire preoperative CT image data; A three-dimensional reconstruction module, used for converting the image data collected by the data acquisition module into a three-dimensional model; A coordinate construction module is used to establish a unified virtual coordinate system in the three-dimensional model and to establish several key positioning points in the virtual coordinate system; Virtual cutting module, used to simulate different paths and angles to perform pathological sections on 3D models; Image tracking module, used to capture real-time image data inside the patient's body during surgery and fuse the image data with the 3D model based on key positioning points; An image processing module, used for collecting and processing images of pathological sections made using the paths and angles simulated in the virtual cutting module; The spatial verification module is used to match the image data processed by the image processing module with the three-dimensional model in spatial position, and to verify whether the spatial correspondence between the preoperative plan and the actual surgical result is accurate by calculating the similarity between the slice image and the corresponding area of ​​the three-dimensional model and the coincidence index of the lesion position.

2. The detection system for assisting accurate positioning of chest lesions according to claim 1, characterized in that: The data collection module is also used to collect patient information, medical history records and drug allergy history.

3. The detection system for assisting accurate positioning of chest lesions according to claim 1, characterized in that: The specific steps of the key positioning point establishment process are as follows: A1: Based on the anatomical features of the 3D model, the image processing technology is used to automatically identify and mark the boundaries and center points of the anatomical landmarks as the first marking points; A2: According to the needs of surgical planning, combined with the CT impact data collected in the data acquisition module, several representative points are selected from the first marking points and recorded as the second marking points; A3: Based on the simulated cutting path and angle in the virtual cutting module, verify and adjust the position of the second marking point, and record the verification and adjustment results as key positioning points.

4. The detection system for assisting accurate positioning of chest lesions according to claim 3, characterized in that: Anatomical landmarks in A1 include, but are not limited to, ribs, vertebrae, major blood vessels, and lung lobe boundaries.

5. The detection system for assisting accurate positioning of chest lesions according to claim 3, characterized in that: The first marking points in A1 include, but are not limited to: the center of the lesion, a specific position of the edge of the lesion, an intersection of important anatomical structures adjacent to the lesion, and an obstacle point adjacent to the lesion.

6. The detection system for assisting accurate positioning of chest lesions according to claim 1, characterized in that: The spatial position matching process in the spatial verification module is: B1: Through virtual coordinates, the two-dimensional image of the pathological slice is mapped to the corresponding plane of the three-dimensional model constructed in the three-dimensional reconstruction module according to the transformation matrix obtained by registration; B2: Using key positioning points, check whether the anatomical features in the slice image are consistent with the key positioning points of the corresponding area in the 3D model, and adjust the correspondence between the pathological slice and the 3D model; B3: For the slice image mapped onto the 3D model, calculate the similarity between it and the corresponding area of ​​the 3D model; B4: Based on the confirmation of the similarity between the corresponding areas of the slice image and the 3D model, the coincidence of the lesion position in the slice image and the 3D model is evaluated; B5: Based on the results of similarity calculation and lesion position coincidence evaluation, spatial position matching verification is performed.

7. The detection system for assisting accurate positioning of chest lesions according to claim 6, characterized in that: The similarity calculation process in B3 is: based on the similarity measurement method of image features, the grayscale distribution, texture features or structural information of the corresponding area of ​​the pathological slice image and the three-dimensional model are compared to calculate the similarity score between the two; The similarity measurement methods include but are not limited to: mutual information, normalized cross-correlation or structural similarity index.

8. The detection system for assisting accurate positioning of chest lesions according to claim 6, characterized in that: The calculation process of the coincidence degree in B4 is as follows: marking the boundaries of the lesion location in the corresponding areas of the pathological slice image and the three-dimensional model respectively; The spatial geometry algorithm is used to calculate the ratio of the overlapping area or volume between the two boundaries to the total area or volume of the lesion, which is the coincidence degree of the lesion location.

9. The detection system for assisting accurate positioning of chest lesions according to claim 1, characterized in that: It also includes a user interaction module, which is used to display the three-dimensional model, virtual cutting path, intraoperative images and spatial verification results, and provide an operation interface for users to input instructions and parameters.

10. The detection system for assisting accurate positioning of chest lesions according to claim 1, characterized in that: It also includes a data management module for automatically recording and storing all acquired imaging data, processing results, preoperative planning information, and real-time intraoperative data. It also integrates a data export function for exporting key data into a common format.

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