A detection system that assists in the precise positioning of chest lesions

Abstract

The present invention relates to the field of medical devices, and specifically to a detection system for assisting in the precise positioning of chest lesions, comprising a data acquisition module for acquiring preoperative CT image data; a three-dimensional reconstruction module for converting data into a three-dimensional model; a coordinate construction module for establishing a unified virtual coordinate system and determining key positioning points; a virtual cutting module for simulating different paths and angles for pathological sectioning of the three-dimensional model; an image tracking module for capturing image data from within the patient's body in real time during surgery and fusing the image data with the three-dimensional model based on key positioning points; an image processing module for image acquisition and processing of pathological sections; and a spatial verification module for spatially matching image data with the three-dimensional model, and calculating the similarity between the image and the corresponding area and the overlap index of the lesion position. The present invention aims to achieve precise spatial position correspondence between the lesion area in the preoperative CT image and the actual resected pathological section during surgery.

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 thoracic diseases, ensuring accurate positioning of the lesion is a key factor in surgical success. Due to the complexity of the thoracic anatomy, which includes multiple important organs, blood vessels, and nerves, precise positioning of the lesion is crucial for reducing surgical risks, protecting normal tissue, and improving treatment outcomes.

[0003] Traditional surgical methods primarily rely on the physician's clinical experience, direct intraoperative observation, and palpation to determine the location of the lesion. While this approach can meet surgical needs to a certain extent, it has significant limitations and deficiencies. First, the physician's experience and subjective judgment can lead to positioning errors, making identification of lesions with blurred boundaries or deep locations particularly difficult. Second, direct intraoperative observation is limited by the visual field and obstructions from anatomical structures, making it difficult to fully and accurately assess the extent of the lesion and its relationship to surrounding tissues.

[0004] The rapid development of medical imaging technology, especially the widespread use of computed tomography (CT), has made it possible to accurately reconstruct and locate lesions before surgery. CT images, with their high resolution and clear anatomical visualization, provide physicians with a wealth of lesion information. However, while preoperative CT images can provide detailed information on lesion location and morphology, accurately translating this information into the actual resection location 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 the 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 address the above-mentioned issues, the present invention provides a detection system that assists in the precise positioning of chest lesions. This system aims to achieve precise spatial correspondence between the lesion area in preoperative CT images and the actual resected pathological slices during surgery by integrating advanced medical image processing, three-dimensional reconstruction, intraoperative image tracking, and spatial position matching technologies, thereby improving the accuracy and safety of surgical treatment of chest diseases.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: A detection system for assisting in the precise positioning of chest lesions, comprising:

[0008] Data acquisition module, used to collect preoperative CT image data;

[0009] A three-dimensional reconstruction module, used to convert 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 for pathological sectioning of 3D models;

[0012] The image tracking module is used to capture real-time imaging data from within 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 is used to collect and process 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 spatial position of the image data processed by the image processing module with the three-dimensional model, and to verify whether the spatial correspondence between the preoperative plan and the actual surgical results 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 this solution is as follows: Preoperative imaging data of the patient's chest is acquired using high-precision CT scanning equipment. Subsequently, a 3D reconstruction algorithm is used to convert these 2D imaging data into a 3D model, reconstructing the three-dimensional structure of the patient's chest and providing a basis for subsequent surgical planning. A unified virtual coordinate system is constructed within the 3D model. This coordinate system is used to standardize and locate various anatomical structures and lesion areas within the chest. Using expert knowledge or automated algorithms, several key positioning points are established within the coordinate system. These points are typically important anatomical landmarks or significant features of lesion boundaries. A virtual cutting module is then used to simulate different paths and angles for pathological sectioning of the 3D model.

[0016] During the operation, the image tracking module captures the patient's internal imaging data in real time, and based on the previously established key positioning points, these real-time images are accurately integrated with the 3D model constructed before the operation. This process ensures that the doctor can obtain the accurate location information of the lesion 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 3D model in spatial position. By calculating the similarity between the corresponding areas of the slice image and the 3D 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 assessment 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, as well as the overlap index of the lesion location. This not only verifies the accuracy of preoperative planning but also provides valuable data support during postoperative analysis, helping doctors continuously optimize surgical plans and improve overall treatment quality.

[0019] 2. This solution, through data acquisition, 3D reconstruction, and coordinate construction modules, generates a highly accurate 3D model of the patient's chest and identifies key positioning points within the model. This significantly improves surgical planning accuracy, enabling surgeons to clearly understand the lesion's location and its spatial relationship to surrounding tissues before surgery, enabling more precise procedures and minimizing damage to normal tissue.

[0020] 3. This solution achieves seamless integration between images and real-world data: The image tracking module and image processing module work together to rapidly integrate real-time intraoperative imaging data with the preoperative 3D model. This real-time image fusion technology provides surgeons with immediate feedback on the location of the lesion, enabling timely adjustments to surgical strategies and ensuring the accuracy of the surgical path and resection range.

[0021] 4. This solution, through intuitive 3D models and image fusion technology, allows doctors to more clearly explain the patient's condition, surgical plan, and expected outcomes to their families, enhancing communication and trust between doctors and patients. Furthermore, this highly accurate preoperative planning and intraoperative guidance enhances patients' confidence in the success of the surgery and facilitates postoperative recovery.

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

[0023] Benefits: By comprehensively collecting a patient's personal information, detailed medical history, and drug allergy history, doctors can more accurately assess their health status and surgical risks, enabling them to develop a more personalized treatment plan. This helps avoid potential complications and improves surgical safety and success rates. Collecting drug allergy history is crucial to ensuring patient safety. Before surgery, doctors can learn in detail about a patient's drug allergies, allowing them to avoid administering medications that may cause adverse reactions during surgery. This helps reduce medical errors and improves the quality and safety of healthcare.

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

[0025] A1: 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 first marking points;

[0026] A2: Based on the requirements of surgical planning and 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 more in line with 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, thereby improving 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] Furthermore, 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 more accurately focus on these key areas during automatic recognition. Ribs and vertebrae, as hard tissues, have relatively clear boundaries and are 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 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 point of an important anatomical structure adjacent to the lesion, and an obstacle point adjacent to the lesion.

[0032] Beneficial effects: By clearly defining the lesion center, specific edge locations, and the intersection of adjacent important anatomical structures as the first landmark, 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 landmark helps doctors identify and avoid these potential danger areas before surgery. This greatly reduces the risk of complications caused by accidental damage 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: Using virtual coordinates, the 2D image of the pathological section is mapped to the corresponding plane of the 3D model constructed in the 3D 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 overlap of the lesion location in the slice image and the 3D model is evaluated;

[0038] B5: Based on the results of similarity calculation and lesion location coincidence assessment, perform spatial position matching verification.

[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, enhancing communication and understanding between doctors and patients. After the operation, the doctor 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 direction for future surgical operations.

[0040] Furthermore, the similarity calculation process in B3 is as follows: based on the similarity measurement method of image features, the grayscale distribution, texture features or structural information of the corresponding area of the pathological section 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 characteristics, or structural information, the similarity between pathological slide images and corresponding areas of the 3D model can be more comprehensively assessed. These features manifest themselves uniquely in different types of lesions and anatomical structures, thus providing more accurate matching results. Similarity metrics such as mutual information, normalized cross-correlation, and structural similarity index each have distinct characteristics and advantages. Combining multiple metrics can overcome 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 evaluating surgical outcomes. Doctors can intuitively understand the degree of deviation between the actual surgical outcome and the preoperative plan, thereby more accurately assessing the success of the operation. By comparing the coincidence of pathological slice images with the lesion location in the 3D model, doctors can promptly identify possible deviations during the operation and make adjustments in subsequent operations to improve surgical precision. The results of the coincidence assessment can provide a valuable reference for future surgical planning. Based on the coincidence data of historical operations, doctors can continuously optimize the surgical path and resection range 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 images and spatial verification results, and provide an operation interface for users to input instructions and parameters.

[0047] Beneficial Effects: The user interaction module visualizes the 3D model, allowing doctors to intuitively understand the patient's anatomy and pathology. This helps doctors conduct a more comprehensive preoperative assessment and develop a more scientific and reasonable surgical plan. Furthermore, the real-time display of intraoperative images allows doctors to monitor surgical progress and adjust surgical strategies accordingly. The virtual cutting path demonstration function allows doctors to simulate and optimize the surgical path before surgery, thereby reducing surgical risks and improving surgical precision. Doctors can practice repeatedly in the virtual environment to find the optimal surgical path and resection range, ensuring accuracy at every step during the operation. The user interaction module provides an intuitive and engaging platform for doctor-patient communication. By displaying the 3D model, virtual cutting path, and intraoperative images, doctors can explain the surgical process and expected outcomes in detail to patients and their families, enhancing their trust and confidence in treatment. Patients can also use the user interface to learn more about the surgery, raise questions, and provide suggestions, fostering 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 are properly preserved through automated data recording and storage processes. This avoids data loss due to human negligence or system failures, and ensures the security and integrity of the data. Centrally managed data resources enable doctors and other medical workers to access and query the required information at any time, thereby improving the efficiency of data utilization. Doctors can make more accurate decisions based on historical data and real-time data, and 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, facilitating sharing and communication between different systems and institutions. This helps promote the optimal allocation and collaboration of medical resources, and improves the efficiency and service level of the entire medical industry.

[0050] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0052] Figure 2 A flow chart showing the establishment of key positioning points in an embodiment of a detection system for assisting in the precise 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 according to the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

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

[0056] Example 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 history, 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 3D model and to 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 3D model, image processing technology is used to automatically identify and mark the boundaries and center points of anatomical landmarks, including but not limited to ribs, vertebrae, major blood vessels, and lung lobe boundaries. These 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 location of the lesion edge, the intersection of important anatomical structures adjacent to the lesion, and obstacles adjacent to the lesion.

[0062] A2: Based on the requirements of surgical planning, such as surgical approach and resection range, combined with the CT imaging data collected in the data acquisition module, several representative points are selected from the first marker points and recorded as second marker points; these second marker points should be able to fully reflect the key anatomical features and pathological 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 simulates 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 surrounding important anatomical structures. The Virtual Cutting Module also features high-precision image rendering and computing capabilities, generating realistic pathological section images and 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 made using the paths and angles simulated in the virtual cutting module. By comprehensively applying multiple image processing technologies, the scanning and image processing of real 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 3D model in spatial position, and to verify the accuracy of the spatial correspondence between the preoperative plan and the actual surgical results by calculating the similarity between the slice image and the corresponding area of the 3D 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 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, calculate the similarity between it and the corresponding area of the three-dimensional model; the similarity calculation process is: based on the similarity measurement method of image features, compare the grayscale distribution, texture characteristics or structural information of the pathological slice image and the corresponding area of the three-dimensional model, and calculate the similarity score between the two; the similarity measurement method includes but is not limited to: mutual information, normalized cross correlation or structural similarity index.

[0071] B4: Based on the confirmation of the similarity between the corresponding areas of the slice image and the 3D model, the overlap of the lesion location in the slice image and the 3D model is evaluated. The overlap is calculated as follows: the boundaries of the lesion location are marked in the corresponding areas of the pathological slice image and the 3D model respectively; and the spatial geometry algorithm is used 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 location.

[0072] B5: Based on the results of similarity calculation and lesion location coincidence assessment, perform spatial position matching verification.

[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, basic information such as the patient's name, age, gender, medical history, and drug allergy history is entered. Using high-precision CT scanning equipment, a preoperative CT scan of the patient's chest is performed to obtain high-quality two-dimensional CT image data. Using the three-dimensional reconstruction module, the collected two-dimensional CT image data is converted into a three-dimensional model. This model must accurately reflect the patient's chest anatomical structure, including ribs, vertebrae, major blood vessels, and lung lobes. Through the virtual cutting module, different paths and angles are simulated 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. Using high-precision image rendering and computing power, realistic pathological section images are generated to provide doctors with intuitive visual reference.

[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 preliminary first markers. Based on the surgical planning requirements, several representative points are selected from the first markers as second markers to ensure that these points fully reflect the key anatomical features and pathological conditions of the surgical area. Using the simulated cutting path and angle in the virtual cutting module, the positions of the second markers are verified and adjusted, and ultimately established as key positioning points.

[0076] During surgery, the image tracking module captures real-time imaging data (such as X-rays and CT scans) from within the patient's body. Based on key positioning points, the intraoperative imaging data is integrated with the 3D model to ensure consistency between surgical procedures and preoperative planning.

[0077] The image processing module captures and processes images of pathological sections based on the paths and angles simulated in the virtual cutting module, providing high-quality, high-precision medical image analysis services. The spatial verification module spatially matches the processed image data with the 3D model, calculating the similarity between the slice image and the corresponding area of the 3D model, as well as the overlap of the lesion location. The 2D image of the pathological section is mapped onto the corresponding plane of the 3D model, and the correspondence is adjusted to ensure consistency.

[0078] Based on the image feature similarity metric, the similarity between the slice image and the corresponding area of the 3D model is calculated. The overlap of the lesion location between the two is also evaluated. Based on the similarity and overlap calculation results, the accuracy of the spatial correspondence between the preoperative plan and the actual surgical outcome is analyzed.

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

[0080] Example 2:

[0081] The difference from Example 1 is that it also includes a data management module for automatically recording and storing all collected 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.

[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 securely 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) collects patient imaging data, real-time intraoperative data (such as location information from the surgical navigation system and vital sign monitoring data), and patient information. Preprocessing operations such as denoising, enhancement, and segmentation are performed to improve the accuracy of subsequent processing and analysis. The processed results are stored in a database in an appropriate format (such as images, reports, and statistical charts) for subsequent review and analysis. Ensure that real-time intraoperative data is synchronized with the data management system in real time, allowing doctors to review and adjust treatment strategies at any time during the operation.

[0084] Provides data export functionality to convert key data (such as treatment results, diagnostic reports, and preoperative planning plans) into common formats such as PDF, Excel, and CSV. 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 clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A detection system for assisting in the precise positioning of chest lesions, characterized in that: include: Data acquisition module, used to collect preoperative CT image data; A three-dimensional reconstruction module, used to convert the image data collected by the data acquisition module into a three-dimensional model; The coordinate construction module is used to establish a unified virtual coordinate system in the 3D model and to determine several key positioning points in the virtual coordinate system. The specific steps of the key positioning point establishment process are as follows: A1: Based on the anatomical features of the 3D model, the boundaries and center points of the anatomical landmarks are automatically identified and marked using image processing technology as the first marking points; A2: Based on the requirements of surgical planning and the CT image 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, the position of the second marker is verified and adjusted, and the verification and adjustment results are recorded as key positioning points. The virtual cutting module is used to simulate different paths and angles to perform pathological sections on the 3D model. The image tracking module is used to capture real-time imaging data from within the patient's body during surgery and fuse the image data with the 3D model based on key positioning points; An image processing module is used to collect and process images of pathological sections made using the paths and angles simulated in the virtual cutting module; The spatial verification module is used to spatially match the image data processed by the image processing module with the 3D model. It also verifies the accuracy of the spatial correspondence between the preoperative plan and the actual surgical results by calculating the similarity between the corresponding areas of the slice image and the 3D model and the coincidence index of the lesion position. The spatial position matching process in the spatial verification module is as follows: B1: Using 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 overlap of the lesion location in the slice image and the 3D model is evaluated; B5: Based on the results of similarity calculation and lesion location coincidence assessment, perform spatial position matching verification.

2. The detection system for assisting in 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 in accurate positioning of chest lesions according to claim 1, characterized in that: Anatomical landmarks in A1 include, but are not limited to, ribs, vertebrae, major blood vessels, and lung lobe boundaries.

4. The detection system for assisting in accurate positioning of chest lesions according to claim 1, characterized in that: The first marking points in A1 include, but are not limited to, the center of the lesion, a specific location of the lesion edge, an intersection of important anatomical structures adjacent to the lesion, and an obstacle point adjacent to the lesion.

5. The detection system for assisting in accurate positioning of chest lesions according to claim 1, characterized in that: The similarity calculation process in B3 is as follows: 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 3D model are compared, and the similarity score between the two is calculated. The similarity measurement methods include but are not limited to: mutual information, normalized cross-correlation or structural similarity index.

6. The detection system for assisting in accurate positioning of chest lesions according to claim 1, characterized in that: The calculation process of the coincidence degree in B4 is as follows: the boundaries of the lesion location are marked in the corresponding areas of the pathological slice image and the 3D 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.

7. The detection system for assisting in 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.

8. The detection system for assisting in 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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