A brain image processing method, device and program product based on 3D slicer

Abstract

This application relates to the field of intelligent healthcare, specifically to a brain image processing method, device, and program product based on 3D Slicer. It includes: S1, acquiring patient brain image sequence data; S2, registering the image sequence data using 3D Slicer to obtain a registered image sequence; S3, performing 3D reconstruction based on the registered sequence to obtain a 3D brain model; S4, selecting the sequence used as the center coordinate during registration or the image sequence after registration and coordinate updates; S5, filtering sequences whose scanning range includes the whole brain from the sequence used as the center coordinate during registration or the image sequence after registration and coordinate updates to obtain a filtered image sequence; S6, converting the 3D brain model into a Labelmap file based on the filtered image sequence. This application can import 3D model data reconstructed by modeling software such as Slicer into existing neuronavigation systems, combining the functions of both to achieve higher quality intraoperative navigation, which has significant clinical value.

Description

Technical Field

[0001] This application relates to the field of intelligent healthcare, specifically to brain image processing methods, devices, program products, and computer-readable storage media based on 3D Slicer. Background Technology

[0002] Neuronavigation systems (such as Brainlab) are currently the most important and commonly used intraoperative localization systems in neurosurgery worldwide. They effectively help neurosurgeons accurately locate and properly protect important anatomical structures (such as important brain functional areas, cortical structures, and blood vessels) during surgery, significantly improving surgical quality. However, due to the significant limitations in the segmentation and reconstruction functions of current mainstream neuronavigation systems, they cannot accurately and with high quality reconstruct some cranial nerves (such as the facial nerve), precentral gyrus, and other anatomical structures, thus limiting their role in certain neurosurgical procedures.

[0003] 3D Slicer is a powerful, free, and open-source image post-processing software widely known and used by neurosurgeons worldwide. Numerous studies and reports have documented neurosurgeons' application of 3D Slicer for preoperative reconstruction and to assist in neurosurgical procedures. These studies have confirmed that 3D Slicer effectively assists in various neurosurgical procedures, significantly improving surgical quality and reducing surgical collateral damage and postoperative complications. In particular, the application of 3D Slicer for preoperative reconstruction of structures such as the facial nerve to assist in the surgical resection of intracranial tumors has been widely recognized by neurosurgeons for its significant reduction in surgical collateral damage and improved facial nerve function preservation. However, the intraoperative auxiliary function of the reconstructed 3D images in 3D Slicer is limited to the presentation of the 3D model image on the display screen. The reconstructed 3D images cannot be directly imported into neuronavigation systems that only recognize Digital Imaging and Communications in Medicine (DICOM) format files, thus preventing the use of existing neuronavigation systems for intracranial tumor navigation and localization. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a brain image processing method based on 3D Slicer. This method imports three-dimensional model images of structures such as the facial nerve, reconstructed using 3D Slicer software, into an existing neuronavigation system to combine the advantages of both. Ultimately, this enables navigation and localization of the facial nerve during acoustic neuroma surgery. Specifically, it includes:

[0005] S1. Obtain the patient's brain imaging sequence data;

[0006] S2. The image sequence data is registered using a 3D Slicer to obtain a registered image sequence.

[0007] S3. Based on the registered sequence, perform three-dimensional reconstruction to obtain a three-dimensional model of the brain;

[0008] S4. Select the sequence that serves as the center coordinate during registration or the image sequence that has been registered and whose coordinates have been updated.

[0009] S5. Select the whole brain scan range from the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated to obtain the filtered image sequence.

[0010] S6. The three-dimensional brain model is converted into a Labelmap file based on the selected image sequence. Further, S5 includes performing thickness filtering on the selected sequences, selecting the sequence with the thinnest scanning layer or the smallest voxel to obtain a thickness-filtered image sequence. The three-dimensional brain model and the thickness-filtered image sequence are then converted into a Labelmap file.

[0011] The registration is the registration of each image sequence with the image sequence that serves as the center coordinate before modeling;

[0012] The registration process involves unifying the coordinates of all image sequences before establishing the brain model, ensuring that the coordinate system of the image sequence data is consistent with the coordinate system of the three-dimensional brain reconstruction model.

[0013] The brain model is composed of models of surgically related anatomical structures obtained by three-dimensional reconstruction of various anatomical structures related to brain and intracranial surgery, and each anatomical structure model is marked with a different color;

[0014] Optionally, the brain and intracranial surgery-related anatomical structures include one or more of the following: brainstem, facial nerve, trigeminal nerve, skull, and veins;

[0015] Optionally, the brain imaging data includes one or more of the following: MRI, CT, or DSA;

[0016] Optionally, based on the brain and intracranial surgery-related anatomical structure models, steps S4-S5 are performed sequentially to obtain the image sequences of each anatomical structure model after screening, and each anatomical structure model is converted into a Labelmap file based on the screened image sequences.

[0017] The purpose of this invention is to provide a method for locating intracranial tumors based on intraoperative neuronavigation, comprising:

[0018] The method uses the 3D Slicer-based brain image processing method to process images and obtain Labelmap files of intracranial tumor surgery-related anatomical structures; wherein, the brain model reconstructed by the 3D Slicer-based brain image processing method in the method for locating intracranial tumors based on intraoperative neuronavigation also includes the tumor and facial nerve.

[0019] The Labelmap file is converted to obtain DICOM data;

[0020] The intraoperative neuronavigation system acquires the DICOM data and the patient's original MRI and CT image data;

[0021] The DICOM data is sequentially fused with the patient's original image data to obtain the fused data;

[0022] The target structure in the fused data is segmented to obtain segmentation data of the surgery-related structure;

[0023] The tumor or nerve is located based on the segmentation data.

[0024] Furthermore, the method also includes data labeling, labeling the DICOM data to obtain a labeled dataset; and intraoperative neuronavigation to acquire the labeled data and patient brain imaging data.

[0025] The tumors mentioned in the method include one or more of the following: acoustic neuroma, trigeminal schwannoma, parasagittal, convexity or functional area meningioma and lymphoma, brain parenchymal astrocytoma, oligodendroglioma, brain metastasis, and brain abscess.

[0026] The purpose of this invention is to provide a computer program product having a computer program / instruction thereon, which is executed by a processor to implement the above-described brain image processing method based on 3D Slicer.

[0027] The purpose of this invention is to provide a computer program product having a computer program / instruction thereon, which is executed by a processor to implement the above-described method for locating intracranial tumors based on intraoperative neuronavigation.

[0028] The purpose of this invention is to provide a computer device having a memory or processor and a computer program / instructions stored in the memory, wherein the computer program / instructions are executed by the processor to implement the above-described brain image processing method based on 3D Slicer.

[0029] The purpose of this invention is to provide a computer device having a memory or processor and a computer program / instructions stored in the memory, wherein the computer program / instructions are executed by the processor to implement the above-described method for locating intracranial tumors based on intraoperative neuronavigation.

[0030] The purpose of this invention is to provide a computer-readable storage medium storing a computer program / instruction thereon, which is executed by a processor to implement the above-described brain image processing method based on 3D Slicer.

[0031] The purpose of this invention is to provide a computer-readable storage medium having a computer program / instruction stored thereon, the computer program / instruction being executed by a processor to implement the above-described method for locating intracranial tumors based on intraoperative neuronavigation.

[0032] Advantages of this invention:

[0033] 1. By combining the advantages of 3D medical models reconstructed by software such as 3Dslicer with the intraoperative navigation and positioning functions of existing neuronavigation systems, the navigation range of existing neuronavigation systems is expanded, and the accuracy of intraoperative navigation and positioning is improved. In particular, for facial nerves that were previously difficult to reconstruct and locate, they can be accurately identified and located, reducing the probability of intraoperative damage to facial nerves and improving the functional preservation rate of facial nerves.

[0034] 2. In the process of generating Labelmap files for 3D models of surgical-related structures using 3Dslicer software, a selection principle for the image sequence (or carrier sequence) on which the 3D model is based was proposed. The selection principle is to select sequences that serve as center coordinates or image sequences that have been registered and updated to ensure that the target model is in the correct spatial position in the corresponding image sequence and is in the correct positional relationship with the surrounding anatomical structures. This also makes it easier for the final DICOM data to be fused with the original image data or other image sequences after being imported into the navigation system.

[0035] 3. Select an image sequence that covers the entire brain to facilitate the smooth integration of the generated DICOM file with other image data after importing it into the navigation system.

[0036] 4. Select the image sequence with the thinnest scan slice or the smallest voxel to improve the image quality of the target structure obtained after segmentation in the neuronavigation system. This can make the segmented 3D image smoother, more continuous, clearer, and more aesthetically pleasing from a morphological perspective. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A schematic diagram of a brain image processing method based on 3D Slicer provided in an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of a brain image processing system based on a 3D Slicer provided in an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of a brain image processing device based on a 3D Slicer provided for an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram showing the different anatomical structures marked with different colors, provided as an embodiment of the present invention.

[0042] Figure 5 This is a schematic diagram illustrating the conversion of a model into a Labelmap file as provided in an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram illustrating the conversion of a Labelmap file to a DICOM file according to an embodiment of the present invention.

[0044] Figure 7 This invention provides DICOM data corresponding to the brain and various anatomical structures fused in a neuronavigation system for embodiments of the invention.

[0045] Figure 8 The neuronavigation system provided in this embodiment of the invention displays images of segmented anatomical structures, wherein the facial nerve image is clear and coherent.

[0046] Figure 9 The facial nerve images segmented by the carrier image sequence without selecting the thinnest slice thickness or the smallest voxel, as provided in the embodiments of the present invention, appear as discontinuous and discontinuous images.

[0047] Figure 10 The brain model provided in this embodiment of the invention did not select a carrier sequence as the center coordinate, did not perform whole-brain screening, and did not perform thickness screening. As a result, the three-dimensional spatial coordinates of the facial nerve image segmented in the end are inaccurate, and the spatial positional relationship with the surrounding structures is inaccurate.

[0048] Figure 11 Three-dimensional models and carrier sequences of other types of intracranial tumors provided in embodiments of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0050] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0051] Figure 1 A schematic diagram of a brain image processing method based on a 3D Slicer provided in this embodiment of the invention specifically includes:

[0052] S1: Acquire sequence data of the patient's brain imaging;

[0053] In one embodiment, the brain imaging sequence data includes one or more of the following: MRI, CT, or DSA.

[0054] S2: The image sequence data is registered using a 3D Slicer to obtain a registered image sequence;

[0055] In one embodiment, 3D Slicer is an open-source software platform primarily used for medical image informatics, image processing, and 3D visualization. Its main functions include:

[0056] 1. Medical Image Viewing and Analysis: 3D Slicer supports various medical imaging data formats, such as CT, MRI, and PET, allowing users to load, browse, compare, and analyze this data.

[0057] 2. 3D Reconstruction and Visualization: It can convert 2D medical image data into 3D models, which is crucial for understanding anatomical structures, pathological changes (such as tumors), and surgical planning.

[0058] 3. Image registration: Allows users to align images from different sources or time points, which is very useful for tracking disease progression or comparing changes before and after treatment.

[0059] 4. Image Segmentation and Labeling: Provides powerful tools for manually or automatically segmenting structures in images, which is crucial for quantitative analysis (such as measuring organ volume). Automatic segmentation is also supported using extensions such as Total Segmentator and MONAI Label.

[0060] In one embodiment, the registration is the registration of the image coordinate system of the image sequence data with the coordinate system of the three-dimensional reconstruction model to align the image structure.

[0061] Before reconstructing the medical model in three dimensions, all image sequences are unified in a coordinate system to ensure that the coordinate system of the finally selected image sequence (carrying sequence) is the same as the coordinate system of the reconstructed three-dimensional model.

[0062] In one embodiment, the brain model is composed of surgically relevant anatomical structure models obtained by three-dimensional reconstruction of various anatomical structures related to brain and intracranial surgery using software such as Slicer, and each anatomical structure model is marked with a different color.

[0063] In one embodiment, the brain and intracranial surgical-related anatomical structures include one or more of the following: brainstem, facial nerve, trigeminal nerve, skull, and veins.

[0064] Optionally, the surgically related anatomical structures include tumors, brainstem, cranial nerves, skull, and veins.

[0065] S3: Based on the registered sequence, a three-dimensional reconstruction is performed to obtain a three-dimensional model of the brain;

[0066] In one embodiment, 3D reconstruction is a technique designed to recover and create a three-dimensional representation of a target object or environment from a series of two-dimensional images, sensor data, or scan data. This process is crucial for understanding, simulating, analyzing, and visualizing real-world objects and scenes. 3D reconstruction technology is widely used in various fields such as medical imaging and engineering design.

[0067] 3D reconstruction typically involves the following key steps:

[0068] 1. Data Acquisition: Scanning the internal structure of the human body, such as through CT or MRI.

[0069] 2. Feature Extraction and Matching: Identifying and matching key feature points in the collected 2D images. Features can be edges, corners, textures, etc., and the relative positions between different views can be calculated using these features.

[0070] 3. 3D Space Reconstruction: The position and orientation of the camera are calculated using matched feature points, and the 3D point cloud is reconstructed using methods such as triangulation or multi-view geometry. A point cloud is a discrete representation of an object's surface in 3D space.

[0071] 4. Surface generation and refinement: Point cloud data can be converted into surface models using various algorithms, such as surface fitting and surface reconstruction algorithms (e.g., Poisson reconstruction, Marching Cubes), to make the model smoother and more continuous.

[0072] 5. Texture mapping: Mapping the texture information of the original image onto the reconstructed 3D model.

[0073] 6. Post-processing and optimization: This includes cleaning the model, filling holes, simplifying the model, and adjusting lighting and materials to improve the quality and usability of the final model.

[0074] S4: Select the sequence that serves as the center coordinate during registration or the image sequence that has been registered and whose coordinates have been updated;

[0075] In one embodiment, corresponding image sequences (carrying sequences) are selected for the three-dimensional model of the brain, tumor, etc., and the sequence used as the center coordinate during registration or the image sequence that has been registered and whose coordinates have been updated are selected.

[0076] S5: Select the image sequence whose scanning range includes the whole brain from the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated.

[0077] In one embodiment, the filtered image sequence is a carrier sequence.

[0078] In one embodiment, S5 further includes performing thickness screening on the selected sequences, screening the sequences with the thinnest scanning layers or the smallest voxels to obtain thickness-screened image sequences, and converting the brain 3D model into a Labelmap file based on the thickness-screened image sequences.

[0079] To further explain, S5 also includes further filtering from the filtered sequences based on the scan layer thickness, selecting the sequence with the thinnest scan layer or the smallest voxel as the thickness-bearing image sequence corresponding to the three-dimensional model, and generating a Labelmap file after format conversion.

[0080] In one embodiment, based on the model of the surgically related anatomical structures such as tumors and cranial nerves, steps S4-S5 are performed sequentially to obtain the image sequence (carrier sequence) on which each structural model is based. The image sequence is then converted into a Labelmap file for each anatomical structure using 3DSlicer software, and then converted into a DICOM format file and exported.

[0081] In one specific implementation, within the 3D Slicer, "Model to LabelMap" is a transformation process that converts a 3D model into a label map. This functionality is particularly useful in certain application scenarios, especially when it is necessary to integrate the geometric information of a structure with image data.

[0082] In one embodiment, in 3D Slicer, users can perform the "Model to LabelMap" conversion through the following steps:

[0083] First, load a three-dimensional model (such as the brain, cranial nerves, etc.) into the 3D Slicer.

[0084] Select the model, find and launch the "Model to LabelMap" module in the "Modules" menu.

[0085] In the module interface, select the corresponding image sequence (carrier sequence) for the 3D model in the "Input volume" option according to steps S4 and S5. In the "Model" option, select the target model to be loaded and converted. Name the generated Labelmap file in "Output Volume" and click "Apply" or the corresponding button to start the conversion process.

[0086] After the conversion is complete, the newly generated label image will appear as a new data node in the 3D Slicer's data browsing library, which can be used for subsequent analysis or visualization.

[0087] In one specific embodiment, the facial nerve and brainstem models reconstructed by 3DSlicer software are converted using the "Model To Label Map" module in 3DSlicer software. In the "Input Volume" option of the "Model To Label Map" module, an image sequence should be selected according to the following principles: (1) Select the sequence that was used as the center coordinate during image data registration in the model making process or the sequence that has been registered to ensure that the model coordinates are consistent with the coordinates of the sequence; (2) Select the sequence whose scanning range includes the whole brain; (3) Select the sequence with the thinnest scanning slice or the smallest voxel.

[0088] The above selection criteria were summarized after testing and comparing all sequences one by one. Failure to follow the above principles may affect the quality of the generated Labelmap file.

[0089] In one specific embodiment, the present invention can also convert the Model into a Labelmap file through the "Mesh To Label Map" module. However, by comparison, it was found that this module is more complicated to operate and cannot select the image sequence carrying the Labelmap, which brings many difficulties to the subsequent DICOM data processing. Therefore, the "Model To Label Map" module is the best method to convert the 3D Model into a Labelmap file.

[0090] S6: The three-dimensional brain model is converted into a Labelmap file based on the selected image sequence.

[0091] In one embodiment, image sequences are selected for each 3D model before format conversion, and different models can ultimately select the same sequence as the carrier sequence.

[0092] In one embodiment, after acquiring the patient's brain image sequence data, the image coordinates of the image sequence are registered with the model coordinates of the three-dimensional model using 3D Slicer software to obtain the registered image sequence, so that the image coordinates and the left side of the model have a unified coordinate transformation relationship. Specifically, the image sequence with the largest scanning range or any image sequence is selected as the center coordinate sequence, and the center coordinates are registered with the model coordinates to obtain the coordinate transformation relationship. Based on the coordinate transformation relationship, the remaining image sequences are registered to obtain the registered image sequence.

[0093] Furthermore, a three-dimensional brain model is obtained by performing three-dimensional reconstruction in the model coordinate system based on the registered image sequence. The three-dimensional brain model includes various anatomical structures related to craniocerebral surgery.

[0094] Meanwhile, the sequence that was used as the center coordinate during registration or the sequence that has been registered and updated is selected from the image sequence, and then the sequence with the scanning range covering the whole brain is further filtered to obtain the filtered sequence image.

[0095] Finally, the selected sequence images are used as the carrier sequence for the 3D model and exported along with the 3D brain model to convert them into labelmap files.

[0096] Among them, the sequences that scan the entire brain also include further screening, with the thinnest or smallest voxel sequence being selected as the filtered sequence image.

[0097] Among them, the anatomical structures in the three-dimensional brain model are obtained by the above screening to obtain the carrier files of each anatomical structure.

[0098] In one embodiment, the brain 3D model is output as a Labelmap file based on the bearer sequence. The selection steps for the bearer sequence (the image sequence after image coordinates and model coordinates are registered) can be any of the following:

[0099] First, select the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated. Then, further filter the sequence whose scanning range covers the whole brain. Finally, filter the sequence with the thinnest layer or the smallest voxel as the filtered image sequence.

[0100] First, select the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated. Then, further filter the sequence with the thinnest layer or the smallest voxel as the filtered image sequence. Then, further filter the sequence whose scanning range covers the whole brain.

[0101] First, sequences that cover the entire brain area are selected. Then, sequences that are used as the center coordinates during registration or sequences that have been registered and updated are selected. Finally, sequences with the thinnest slices or the smallest voxels are selected as the final sequence images.

[0102] First, sequences that cover the entire brain area are selected. Then, sequences with the thinnest slices or the smallest voxels are selected as the final sequence images. Sequences that are used as the center coordinates during registration or sequence images that have been registered and updated are further selected.

[0103] First, the sequence with the thinnest layer or the smallest voxel is selected as the filtered sequence image. Then, the sequence used as the center coordinate during registration or the image sequence after registration and coordinate update is further selected. Then, the sequence with the scanning range covering the whole brain is further selected.

[0104] First, the sequence with the thinnest layer or the smallest voxel is selected as the filtered sequence image. Then, the sequence with the scanning range covering the whole brain is further selected. Finally, the sequence used as the center coordinate during registration or the image sequence after registration and coordinate update is selected.

[0105] In one specific embodiment, the three-dimensional models of the surgically relevant anatomical structures are converted into DICOM files and exported, such as... Figure 4 As shown, each model is displayed in a different color. In the 3D Slicer interface, select "models" from the "Modules" section, and the drop-down list will display the structural models represented by different colors.

[0106] In one specific embodiment, the facial nerve and brainstem models reconstructed by 3DSlicer software are converted into corresponding Labelmap files using the "Model To Label Map" module in 3DSlicer software. Figure 5As shown.

[0107] In one specific embodiment, in the 3D Slicer interface, select "Data" under "Modules," select the newly generated Labelmap file from the drop-down list of the "Subject hierarchy" option, right-click the Labelmap file, and select the "Export to DICOM" module to obtain the DICOM file. Figure 6 As shown.

[0108] In one embodiment, after screening sequences and performing 3D modeling using the method of the present invention, clearer and more precise image data is obtained, such as... Figure 7 , Figure 8 As shown, this technology facilitates subsequent intraoperative navigation surgery, provides a foundation for preoperative surgical planning, reduces surgical risks, and has good clinical application value.

[0109] This disclosure also provides a computer program product or system, including a computer program that, when executed by a processor, implements the steps of the above-described 3D Slicer-based brain image processing method.

[0110] Figure 2 A schematic diagram of a brain image processing system based on a 3D Slicer provided in this embodiment of the invention specifically includes:

[0111] Acquisition module: Acquires sequence data of the patient's brain imaging.

[0112] Registration module: The image sequence data is registered using a 3D Slicer to obtain the registered image sequence;

[0113] Reconstruction module: Based on the registered sequence, perform three-dimensional reconstruction to obtain a three-dimensional model of the brain;

[0114] First filtering module: Select the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated with coordinates;

[0115] The second filtering module: filters out sequences whose scanning range includes the whole brain from the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated.

[0116] Conversion module: The three-dimensional brain model is converted into a Labelmap file based on the selected image sequence.

[0117] Figure 3 A schematic diagram of a brain imaging processing device based on a 3D Slicer provided in this embodiment of the invention specifically includes:

[0118] A memory and a processor; the memory is used to store program instructions; the processor is used to invoke the program instructions, when any of the above-described 3D Slicer-based brain image processing methods are executed.

[0119] The present invention also discloses a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, is any of the above-described brain image processing methods based on 3D Slicer.

[0120] This invention also provides a method for locating intracranial tumors based on intraoperative neuronavigation, comprising:

[0121] The method for locating intracranial tumors based on intraoperative neuronavigation uses the 3D Slicer-based brain image processing method to process images and obtain a Labelmap file of the intracranial tumor sequence; wherein,

[0122] Optionally, the brain model reconstructed in the three-dimensional reconstruction based on the 3D Slicer brain image processing method also includes tumors and facial nerves;

[0123] The Labelmap file is converted to obtain DICOM data;

[0124] The intraoperative neuronavigation system acquires the DICOM data and the patient's intraoperative brain imaging data.

[0125] The DICOM data and the patient's intraoperative brain data are fused together according to the navigation system operation steps to obtain the fused data;

[0126] The target structure in the fused data is segmented to obtain segmentation data of the surgery-related structure, and then saved.

[0127] Based on the segmentation data, the tumor or nerve is located using a navigation system.

[0128] In one embodiment, the method further includes data labeling, labeling the transformed data to obtain a labeled dataset; and intraoperative neuronavigation to acquire the labeled data and patient brain imaging data.

[0129] In one embodiment, the tumor in the method includes one or more of the following: acoustic neuroma, trigeminal schwannoma, astrocytoma, oligodendroglioma, ependymoma, meningioma, metastatic tumor, lymphoma, brain abscess, pituitary adenoma, suprasellar meningioma, craniopharyngioma, astrocytoma, epidermoid cyst, arachnoid cyst, and aneurysm.

[0130] In one embodiment, acoustic neuroma is the most common benign tumor of the posterior fossa, accounting for approximately 80%-90% of tumors in the cerebellopontine angle and approximately 8%-10% of intracranial tumors. It commonly causes symptoms such as tinnitus, hearing loss, and dizziness on the affected side. As the tumor grows, it often compresses surrounding structures such as the brainstem and trigeminal nerve, causing symptoms such as headache, dizziness, and facial numbness. Surgical resection is the primary treatment for acoustic neuroma. Although significant progress has been made in neurosurgical techniques and surgical aids, the rate of facial nerve function preservation after surgery for acoustic neuroma patients remains less than ideal.

[0131] In one specific embodiment, software is used to convert the reconstructed three-dimensional image format of the facial nerve and other structures (such as STereoLithography, STL format) into a DICOM format file that can be imported into the neuronavigation system. Simultaneously, related image information (such as name, image number, etc.) is edited to ensure consistency with the patient's MRI and CT images. The converted file, along with the MRI and CT images, is then imported into the neuronavigation system. The procedures used in neuronavigation, such as fusion and segmentation, are then performed to complete the surgical plan. Ultimately, neuronavigation accurately locates the position and course of the facial nerve during surgery, improving the functional preservation rate of the facial nerve after acoustic neuroma surgery. The specific steps include:

[0132] Step S1: Reconstruct the facial nerve and brainstem models using 3DSlicer software and use the "Model To Label Map" module in 3DSlicer software to convert the models into corresponding Labelmap files.

[0133] Step S21: Create a corresponding DICOM file from the Labelmap file using the "Export to DICOM" function in the "Data" module. Simultaneously, input image information such as "Name" and "Image ID" to ensure consistency with the patient's original MRI and CT images. Finally, export the files and name the DICOM files according to their anatomical structures. It is crucial that the name (including capitalization), gender, date of birth, and image ID remain consistent.

[0134] Step S22: Import the labeled DICOM file and the patient's original CT and MRI data in DICOM format into the same folder.

[0135] Step S3: Import all the DICOM format files into the neuronavigation system, perform fusion, segmentation and other processing in sequence, display the anatomical structures such as tumor, facial nerve, and brainstem in the neuronavigation system, and finally complete and save the surgical plan.

[0136] Step S4: The surgical plan is imported into the neuronavigation intraoperative display screen, showing structures such as the tumor and facial nerve. After preoperative fusion, intraoperative navigation and protection of the facial nerve are achieved.

[0137] In one specific embodiment, the present invention screens sequences with central coordinates during registration or sequences that have already been registered, then screens sequences whose scanning range covers the whole brain, and then selects the sequence with the thinnest scanning layer or the smallest voxel as the carrier sequence based on the above screening. From the image, it can be seen that the intracranial tumor and brain structure are clearly and continuously displayed, with good intraoperative navigation and positioning field of view.

[0138] In one specific embodiment, the present invention establishes a target model by selecting sequences with central coordinates during registration or sequences that have already been registered, and then selecting sequences whose scanning range covers the whole brain. The sequence with the thinnest scanning layer or the smallest voxel is not selected as the carrier sequence. Figure 9 As shown, after the target model (right facial nerve) is converted and imported into the navigation system, the segmented image of the right facial nerve is discontinuous, not smooth, and of low quality.

[0139] In one specific embodiment, the target model that does not perform sequence filtering is as follows: Figure 10 As shown, after the target model is converted and imported into the navigation system, it is very difficult to fuse with the original image data, resulting in fusion errors and ultimately an incorrect spatial position of the right facial nerve.

[0140] In one specific embodiment, the present invention enables real-time localization of the facial nerve during surgery using a neuronavigation system, which helps neurosurgeons protect the facial nerve during surgery and improves the functional preservation rate of the facial nerve after acoustic neuroma surgery.

[0141] In one specific embodiment, the image processing method of the present invention perfectly presents and achieves precise intraoperative navigation and positioning of surgically relevant structures of other intracranial tumors (functional area convexity meningiomas) in the navigation system, such as... Figure 11 As shown.

[0142] This invention can also be used for intraoperative navigation of tumors in functional areas, helping neurosurgeons to accurately identify important structures such as the precentral gyrus, central sulcus, and central sulcus vein, which is beneficial for the protection of these important structures during surgery and reduces the occurrence of postoperative neurological deficits.

[0143] In one specific embodiment, the present invention combines medical modeling software such as 3DSlicer with existing neuronavigation systems, giving full play to their respective functional advantages, and jointly assisting in the surgical resection of intracranial tumors and the protection of important functional structures, thereby improving the quality of neurosurgical procedures.

[0144] In one embodiment, the present invention indirectly expands the types of image data that can be imported into neuronavigation systems, improves the functionality of existing navigation systems without increasing additional economic costs, and expands the application scope of neuronavigation systems in the field of neurosurgery, making it easier to promote in a wide range of hospitals.

[0145] In one specific embodiment, a clinical case:

[0146] Patient: Ms. XX, age XX, left acoustic neuroma, from the Second Hospital of Hebei Medical University.

[0147] (1) Data acquisition and model reconstruction: Collect raw data from thin-slice temporal bone CT, thin-slice T1-weighted MRI, diffusion tensor imaging, etc., import them into 3DSlicer software, use the “Segment Editor” module to create three-dimensional model images of structures such as tumors, veins, skull, brainstem, and trigeminal nerve, and use the function options in the “Diffusion” module to create model images of facial nerve.

[0148] (2) Using the “Model To Label Map” module in 3DSlicer software, the corresponding label maps of the tumor, vein, skull, facial nerve and other structures are created respectively.

[0149] (3) In the 3DSlicer software, use the "Export to DICOM" function in the "Data" module to create a DICOM file with the corresponding structure. Enter image information such as "name" and "image number" into the file to ensure consistency with the patient's original MRI and CT images. Finally, export the files and name the DICOM files according to their anatomical structures. Place the newly created DICOM file and the original MRI and CT data in the same folder.

[0150] (4) Import all the data in the folder into the neuronavigation system (Brainlab neuronavigation), and merge all the DICOM data one by one according to the steps. After merging, use the Segmentation function option in the neuronavigation system to reconstruct the tumor, skull, facial nerve and other structures in different colors. Save the surgical plan after it is completed.

[0151] (5) Import the saved plan into the display of the neuronavigation system to present images of the tumor, skull, facial nerve and brainstem. After infrared navigation fusion registration, the facial nerve is located during the operation and confirmed by electrophysiological monitoring under a microscope. This further strengthens the protection of the facial nerve during the operation and realizes intraoperative navigation and localization of the facial nerve in acoustic neuroma.

[0152] This invention also provides a computer program product having a computer program / instruction thereon, which is executed by a processor to implement the method for locating intracranial tumors based on intraoperative neuronavigation.

[0153] The present invention also discloses a computer device having a memory or processor and a computer program / instructions stored in the memory, wherein the computer program / instructions are executed by the processor to implement the above-described method for locating intracranial tumors based on intraoperative neuronavigation.

[0154] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, represents any of the above-described methods for locating intracranial tumors based on intraoperative neuronavigation.

[0155] The verification results of this verification embodiment show that assigning inherent weights to indications can improve the performance of this method compared to the default settings. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated; the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of this embodiment. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0156] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer.

[0157] In readable storage media, the aforementioned storage medium can be a read-only memory, a disk, or an optical disk, etc. The computer device provided by the present invention has been described in detail above. For those skilled in the art, based on the ideas of the embodiments of the present invention, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for locating an intracranial tumor based on intraoperative neuronavigation, characterized in that, include: S1. Obtain the patient's brain imaging sequence data; S2. The image sequence data is registered with the image coordinates of the image sequence and the model coordinates of the three-dimensional model using a 3D Slicer to ensure that the coordinate system of the image sequence data is consistent with the coordinate system of the brain three-dimensional reconstruction model. The sequence with the largest scanning range in the image sequence is selected as the center coordinate sequence, and the center coordinates are registered with the model coordinates to obtain the coordinate transformation relationship. Based on the coordinate transformation relationship, the remaining image sequences are registered to obtain the registered image sequence. The registration is the registration of the image coordinate system of the image sequence data with the coordinate system of the three-dimensional reconstruction model to match the image structure. S3. Based on the registered sequence, perform three-dimensional reconstruction to obtain a three-dimensional model of the brain; S4. First, select the sequence that serves as the center coordinate during registration or the image sequence that has been registered and whose coordinates have been updated. S5. Then, from the sequence that was used as the center coordinate during registration or the image sequence that has been registered and updated, the sequence whose scanning range includes the whole brain is selected to obtain the selected image sequence; then, the thickness is selected from the selected sequence to select the sequence with the thinnest scanning layer or the smallest voxel to obtain the thickness-selected image sequence. S6. The image sequence of each anatomical structure in the three-dimensional brain model after thickness screening is used as the thickness-bearing image sequence corresponding to the three-dimensional model and converted into a Labelmap file. The Labelmap file of the anatomical structure related to intracranial tumor surgery was obtained; the three-dimensional reconstructed brain model also includes the tumor, arteries, and facial nerve. The Labelmap file is converted to obtain DICOM data; The intraoperative neuronavigation system acquires the DICOM data and the patient's original MRI and CT images. The DICOM data is sequentially fused with the patient's original image data to obtain the fused data; The target structure in the fused data is segmented to obtain segmentation data of the surgery-related structure; The tumor or nerve is located based on the segmentation data.

2. The method of locating an intracranial tumor based on intraoperative neurologic navigation according to claim 1, wherein, The brain model consists of surgically relevant anatomical structure models obtained by three-dimensional reconstruction of various anatomical structures related to brain and intracranial surgery, and each anatomical structure model is marked with a different color.

3. The method for locating intracranial tumors based on intraoperative neuronavigation according to claim 2, characterized in that, The anatomical structures related to brain and intracranial surgery include one or more of the following: brainstem, facial nerve, trigeminal nerve, skull, and veins.

4. The method of locating an intracranial tumor based on intraoperative neurologic navigation of claim 1, wherein, The brain imaging sequence data includes one or more of the following: MRI, CT, or DSA.

5. The method for locating intracranial tumors based on intraoperative neuronavigation according to claim 2, characterized in that, Based on the anatomical structure models related to brain and intracranial surgery, steps S4-S5 are performed sequentially to obtain the image sequences after screening of each anatomical structure model. The image sequences after screening of each anatomical structure model are then converted into Labelmap files.

6. The method for locating intracranial tumors based on intraoperative neuronavigation according to claim 1, characterized in that, The method also includes data labeling, which labels the DICOM data to obtain a labeled dataset; intraoperative neuronavigation acquires the labeled data and patient brain imaging data.

7. The method for locating intracranial tumors based on intraoperative neuronavigation according to claim 1, characterized in that, The tumors mentioned in the method include one or more of the following: acoustic neuroma, trigeminal schwannoma, parasagittal, convex or functional area meningioma and lymphoma, parenchymal astrocytoma, oligodendroglioma, brain metastasis, and brain abscess.

8. A computer program product having a computer program / instructions embodied thereon, characterized in that, The computer program / instructions are executed by the processor to implement the method for locating intracranial tumors based on intraoperative neuronavigation as described in any one of claims 1-7.

9. A computer device having a memory and a processor and a computer program / instructions stored in the memory, wherein, The computer program / instructions are executed by the processor to implement the method for locating intracranial tumors based on intraoperative neuronavigation as described in any one of claims 1-7.

10. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the method for locating intracranial tumors based on intraoperative neuronavigation as described in any one of claims 1-7.

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