Human body digital map construction system, electronic equipment and storage medium

Abstract

The invention discloses a human body digital map construction system, electronic equipment and a storage medium, and the system comprises a data obtaining module which is used for obtaining a CT or MRI image of a tree structure in a human body; the image segmentation module is used for segmenting the image based on graphics and a tree topological structure modeling algorithm or a deep learning algorithm to obtain a tree target structure; the structure extraction module is used for extracting a central trunk and a branch structure from the tree-shaped target structure; the feature analysis module is used for analyzing and extracting features of the central trunk and the branch structure to obtain feature information; and the map generation module is used for generating a human body digital map based on the central trunk, the branch structure and the feature information. According to the technical scheme, the in-vivo tree structure can be accurately described and positioned, the method is used for personalized health analysis and diagnosis, and the accuracy and efficiency of medical diagnosis and treatment are improved.

Description

Technical Field

[0001] This invention relates to the field of medical technology, specifically to a human body digital map construction system, electronic device, and storage medium. Background Technology

[0002] Blood vessels are typically characterized by small diameter, long and narrow shape, excessive tortuosity, and intricate structure. Common vascular diseases include vascular calcification, tumors, stenosis, and plaques. Vascular extraction is the foundation for the diagnosis of vascular diseases.

[0003] Extracting tree-like structures such as blood vessels, bone marrow, and nerves from medical images manually is a very time-consuming and laborious task. Therefore, the extraction of tree-like structures such as blood vessels, bone marrow, and nerves using computer-aided diagnosis has become a current research hotspot. However, existing methods have problems such as low accuracy in segmenting blood vessels or nerves and inaccurate description of structures or features. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a human body digital map construction system, electronic device and storage medium that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a human body digital map construction system is provided, the system comprising:

[0006] The data acquisition module is used to acquire CT or MRI images of the tree-like structures inside the human body;

[0007] The image segmentation module is used to segment the image based on computer graphics and tree topology modeling algorithms or deep learning algorithms to obtain a tree-like target structure.

[0008] The structure extraction module is used to extract the central trunk and branch structures from the tree-like target structure;

[0009] The feature analysis module is used to analyze and extract the features of the central trunk and branch structures to obtain feature information;

[0010] The map generation module is used to generate a digital map of the human body based on the central trunk and branch structure and the feature information.

[0011] In some embodiments, the system further includes a data preprocessing module for denoising, grayscale conversion, or enhancement of the acquired images.

[0012] In some embodiments, the image segmentation module is further configured to:

[0013] The image is segmented using a trained convolutional neural network, recurrent neural network, or U-net series network to accurately segment the tree-like target structure of the human body.

[0014] In some embodiments, the structure extraction module is further used for:

[0015] Based on the segmented tree-like target structure, the centerline of the blood vessel or nerve is determined, and the central trunk and branch structure are extracted from the tree-like target structure using distance transformation and skeletonization algorithms.

[0016] In some implementations, the feature analysis module is further used for:

[0017] The diameter of the central trunk and branch structures at different locations, the length of each branch, the bifurcation angle, the width and height of non-circular structures, or the number of branches are measured.

[0018] In some implementations, the system further includes a digital encoding module for digitally encoding and naming the trunks, branches, and nodes in the central trunk and branch structure.

[0019] In some implementations, the digital human body map generated by the map generation module specifically includes at least one of the following:

[0020] Human neural map;

[0021] Map showing the distribution of the trachea and bronchi in the lungs;

[0022] Map showing the distribution of major arteries and veins in the lungs;

[0023] Diagram showing the distribution of blood vessels within the pulp chamber;

[0024] Distribution and morphology of the dental pulp cavity;

[0025] Distribution diagram of the entire dentition;

[0026] Morphology and distribution of oral and craniofacial bone tissue;

[0027] Map showing the course of lymphatic vessels and distribution of lymph nodes in the human body;

[0028] Morphological features of the occlusal surface of a tooth;

[0029] Diagram of the pathways of the mandibular nerve canal and blood vessels;

[0030] Diagram of the distribution and course of intestinal blood vessels;

[0031] Map showing the distribution of vascular branches in the brain.

[0032] In some implementations, the system is used for at least one of the following purposes:

[0033] It is used to diagnose vascular and neurological diseases by observing abnormal structures in the human body digital map.

[0034] This helps doctors understand the distribution and direction of blood vessels or nerves in a patient's digital body map, enabling them to develop more precise surgical plans and reduce surgical risks.

[0035] Used in interventional treatment procedures, the digital map of the human body provides doctors with real-time vascular navigation, guiding them to accurately place catheters.

[0036] Used in the drug development process, the effects of drugs on the vascular system are assessed based on the aforementioned human digital map;

[0037] Used in rehabilitation medicine, it helps doctors assess the recovery of patients' vascular function and develop personalized rehabilitation plans;

[0038] Used for the development of digital design and planning guidance indicators for robotic surgical navigation and path planning;

[0039] Used in medical education and health management, it can enhance visual memory or clinical teaching by presenting two-dimensional or three-dimensional neural or vascular flow, or help develop health management programs.

[0040] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the following operations:

[0041] To obtain CT or MRI images of the dendritic structures within the human body;

[0042] The image is segmented based on computer graphics and tree topology modeling algorithms and / or deep learning algorithms to obtain a tree-like target structure;

[0043] Extract the central trunk and branch structures from the tree-like target structure;

[0044] The features of the central trunk and branch structures are analyzed and extracted to obtain feature information;

[0045] A digital map of the human body is generated based on the central trunk and branch structure and the feature information.

[0046] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, which, when executed by a processor, perform the following steps:

[0047] To obtain CT or MRI images of the dendritic structures within the human body;

[0048] The image is segmented based on computer graphics and tree topology modeling algorithms and / or deep learning algorithms to obtain a tree-like target structure;

[0049] Extract the central trunk and branch structures from the tree-like target structure;

[0050] The features of the central trunk and branch structures are analyzed and extracted to obtain feature information;

[0051] A digital map of the human body is generated based on the central trunk and branch structure and the feature information.

[0052] The technical effects that can be obtained by the embodiments of the present invention are as follows:

[0053] The embodiments of the present invention can achieve precise description and localization of tree-like structures such as blood vessels, bone marrow, and nerves in the body, for personalized health analysis and diagnosis, thereby improving the accuracy and efficiency of medical diagnosis and treatment.

[0054] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0056] Figure 1 A schematic diagram of a human body digital map construction system according to an embodiment of the present invention is shown;

[0057] Figure 2 A schematic diagram of the morphology of dental pulp tissue according to an embodiment of the present invention is shown;

[0058] Figure 3 A schematic diagram of the structure of blood vessels in lung tissue according to an embodiment of the present invention is shown;

[0059] Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0060] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0061] Figure 1 A human body digital map construction system 100 according to an embodiment of the present invention is shown, the system 100 comprising:

[0062] Data acquisition module 110 is used to acquire CT or MRI images of the tree-like structures inside the human body;

[0063] Image segmentation module 120 is used to segment the image based on computer graphics and tree topology modeling algorithms or deep learning algorithms to obtain a tree-like target structure;

[0064] The structure extraction module 130 is used to extract the central trunk and branch structures from the tree-like target structure;

[0065] Feature analysis module 140 is used to analyze and extract the features of the central trunk and branch structures to obtain feature information;

[0066] The map generation module 150 is used to generate a digital map of the human body based on the central trunk and branch structure and the feature information.

[0067] The human digital map construction system disclosed in this embodiment is mainly used to construct digital maps of tree-like structures in the human body, such as blood vessels, nerves, and bone marrow. It can accurately describe and locate the aforementioned tree-like structures, and is particularly suitable for personalized health analysis and diagnosis, thereby improving the accuracy and efficiency of medical diagnosis and treatment.

[0068] In some embodiments, the system further includes a data preprocessing module for denoising, grayscale conversion, or enhancement of the acquired image.

[0069] Image denoising is the process of reducing noise in digital images to improve image quality. Noise is a significant factor interfering with images and may originate from imaging equipment or the external environment. Image denoising methods include adaptive Wiener filters, median filters, morphological noise filters, and wavelet denoising. Grayscale conversion, in the RGB model, means that if R = G = B, then color represents a grayscale color. The value of R = G = B is called the grayscale value. Therefore, each pixel in a grayscale image only needs one byte to store the grayscale value (also known as the intensity value or brightness value), with a grayscale range of 0-255. Image enhancement algorithms are commonly used to adjust the brightness, contrast, saturation, and hue of images to increase sharpness and reduce noise. Image enhancement often involves a combination of multiple algorithms.

[0070] In some embodiments, the image segmentation module 120 is further configured to:

[0071] The image is segmented using a trained convolutional neural network, recurrent neural network, or U-net series network to accurately segment the tree-like target structure of the human body.

[0072] Specifically, the above training process includes: acquiring training data, which includes CT or MRI images, the images including various regions of a tree-like structure such as blood vessels, bone marrow, or nerves; inputting the training data into a structural recognition model built on a 3DUNet network for training to obtain a structural recognition model, the structural recognition model being used to output prediction results for each of the aforementioned regions in the image, the prediction results being used to characterize the location information and category of each of the aforementioned regions; and based on the network structure of the prediction results, the network structure being used to characterize the structural information of the tree-like tissue in the image.

[0073] Optionally, the structure recognition model built based on the 3DUNet network includes convolution, max pooling, skip connections, and upsampling; convolution uses a 3*3*3 kernel to better extract and fuse data features; max pooling uses a 2*2 kernel with a stride of 2 to reduce image size and expand the convolutional field of view; skip connections fuse data from the same encoder stage with data from the encoder stage through concatenation to supplement information; upsampling uses image interpolation to enlarge the image and finally obtain the segmentation result.

[0074] In some embodiments, the structure extraction module 130 is further configured to:

[0075] Based on the segmented tree-like target structure, the centerline of the blood vessel or nerve is determined, and the central trunk and branch structure are extracted from the tree-like target structure using distance transformation and skeletonization algorithms.

[0076] The implementation process of the structure extraction module includes: First, using a topological sparsification algorithm, the tree-like target structure is transformed into a skeleton-based representation, simplifying it into a simpler structure for easier subsequent processing and analysis. Second, based on topological refinement, tracing, shortest path, or distance transform methods, the sparsified tree-like target structure is analyzed and processed to extract the pipeline centerline. Then, the mapping relationship between the sparsified tree-like target structure and the pixels after image thresholding is calculated, and the extracted pipeline centerline is mapped to the pixels in the blood vessel image after image thresholding. Finally, based on the sparsified tree-like target structure and pixel branch allocation results, various features are calculated from the tree-like target structure, including the diameter, length, curvature, branches, and intersections of the pipeline.

[0077] It should be noted that when extracting the central trunk and branch structure of the tree-like target structure, the nodes and edges of the tree-like target structure are obtained respectively. Then, information such as pipeline morphology features, pipeline division regions, pipeline network hierarchy, and blood flow direction are embedded as graph attributes into the edges to obtain the network structure of the tree-like target structure.

[0078] The node features can include node number, node coordinates, diameter, length, number of nodes, number of branches, etc. The edge features can include edge number, starting node number, ending node number, length, distance, curvature, volume, average cross-section, pipe diameter, length, bifurcation angle, average roundness, starting node degree, and blood flow direction. For non-circular structures, height, width, etc. can also be included.

[0079] In some embodiments, the feature analysis module 140 is further configured to:

[0080] Detailed geometric feature information is generated by measuring the diameter of the pipes at different locations of the central trunk and branch structures, the length of each branch, the bifurcation angle, the width and height of non-circular structures, or the number of branches.

[0081] As can be seen from the above embodiments, the specific geometric feature information obtained includes, but is not limited to, the diameter of the pipeline, the length of each branch, the bifurcation angle, the width and height of the non-circular structure, and the number of the above-mentioned branches.

[0082] In some implementations, the system further includes a digital encoding module for digitally encoding and naming the trunks, branches, and nodes in the central trunk and branch structure.

[0083] For example, the following human tissues can be coded: trachea, bronchi, pulmonary vessels, aorta, veins, and intestinal vessels; distribution of vascular branches within the dental pulp cavity, distribution and morphological analysis of the pulp cavity for analyzing pulp health, presence of caries on the occlusal surface of teeth, analysis of the morphological characteristics of the segmented surface, and the course of blood vessels in the mandibular nerve canal; characteristic analysis of blood vessels in different locations, distribution and naming of cerebral vascular branches; and the overall vascular branches of the human body, with each branch given a separate name, named according to the main trunk and branches. The tissues or anatomical structures along different main trunks and branches are characterized, deeply integrated with anatomy, and analyzed in conjunction with human health to construct a personalized digital health human body map.

[0084] In some embodiments, the digital human body map generated by the map generation module 150 specifically includes at least one of the following:

[0085] Human neural map;

[0086] Map showing the distribution of the trachea and bronchi in the lungs;

[0087] Map showing the distribution of major arteries and veins in the lungs;

[0088] Diagram showing the distribution of blood vessels within the pulp chamber;

[0089] Distribution and morphology of the dental pulp cavity;

[0090] Distribution diagram of the entire dentition;

[0091] Morphology and distribution of oral and craniofacial bone tissue;

[0092] Map showing the course of lymphatic vessels and distribution of lymph nodes in the human body;

[0093] Morphological features of the occlusal surface of a tooth;

[0094] Diagram of the pathways of the mandibular nerve canal and blood vessels;

[0095] Diagram of the distribution and course of intestinal blood vessels;

[0096] Map showing the distribution of vascular branches in the brain.

[0097] In one optional specific embodiment, combined with Figure 2 As shown, the analysis of oral pulp health includes:

[0098] 1. By segmenting the dental pulp tissue and extracting its boundary information, the three-dimensional surface structure of the dental pulp can be reconstructed;

[0099] 2. Calculate the texture information of the extracted dental pulp;

[0100] 3. Calculate the morphological characteristics of the extracted dental pulp, such as length, width, surface curvature, and branching structure features.

[0101] Figure 2 This is a three-dimensional reconstruction of segmented dental pulp tissue. In the figure, A is the pulp chamber, B is the pulp horn, and C is the root canal.

[0102] In one optional specific embodiment, after obtaining the segmented structure of the lung tissue, mathematical methods are used to describe its main trunk and branches. This combines imaging features with mathematical description methods (such as stent extraction, branch description methods, and characteristics of each branch) to obtain... Figure 3 The diagram shows the lung tissue structure.

[0103] In some embodiments, the system may be used for at least one of the following purposes:

[0104] It is used to diagnose vascular and neurological diseases by observing abnormal structures in the human body digital map, making diagnoses and characterizations, and deeply integrating them with anatomy and human health for in-depth analysis.

[0105] This technology helps doctors understand the distribution and direction of blood vessels or nerves in a patient's digital body map, enabling them to develop more precise surgical plans and reduce surgical risks. For example, it can be used to process facial blood vessel data in real time and detect facial paralysis, providing immediate feedback. Then, based on individual facial features and historical data, it can provide personalized diagnostic and treatment recommendations.

[0106] Used in interventional treatment procedures, the digital map of the human body provides doctors with real-time vascular navigation, guiding them to accurately place catheters.

[0107] Used in the drug development process, the effects of drugs on the vascular system are assessed based on the aforementioned human digital map;

[0108] Used in rehabilitation medicine, it helps doctors assess the recovery of patients' vascular function and develop personalized rehabilitation plans;

[0109] Used for the development of digital design and planning guidance indicators for robotic surgical navigation and path planning;

[0110] Used in medical education and health management, by presenting two-dimensional or three-dimensional neural or vascular flow, it helps improve visual memory, clinical teaching and clinical thinking, or assists in the development of health management plans.

[0111] In an optional embodiment, human anatomy is presented in full three dimensions through a digital human body map, including the twelve major systems of the male and female bodies. Preferably, it has functions such as real-time presentation of nerves, blood vessels, and lymphatic flow, as well as deep replication of the human body and integration of physiological and medical images, which helps to improve the visual display effect.

[0112] The beneficial effects of the above embodiments include, but are not limited to:

[0113] 1. Medical Diagnosis: Digital maps can provide doctors with an intuitive view of the internal structure, helping them to diagnose diseases more accurately. For example, in pulp health diagnosis, digital maps can clearly show the distribution of blood vessel branches within the pulp cavity, thereby determining the health status of the pulp.

[0114] 2. Surgical Planning: Before surgery, doctors can use digital maps to understand the patient's internal structure and develop a more precise surgical plan. This helps reduce surgical risks and increase the success rate of the surgery.

[0115] 3. Medical Research: Digital maps can provide a wealth of structural data for medical research, helping researchers better understand the complex internal structure of the human body, thereby promoting medical progress.

[0116] 4. Teaching demonstration: In medical education, digital maps can serve as an intuitive teaching tool to help students better understand human anatomy.

[0117] It should be noted that:

[0118] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0119] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0120] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0121] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0122] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0123] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the enamel segmentation system for an independent tooth according to embodiments of the present invention. The present invention can also be implemented as a device or system program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0124] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can perform the operation steps corresponding to any of the above-described systems:

[0125] To obtain CT or MRI images of the dendritic structures within the human body;

[0126] The image is segmented based on computer graphics and tree topology modeling algorithms or deep learning algorithms to obtain a tree-like target structure.

[0127] Extract the central trunk and branch structures from the tree-like target structure;

[0128] The features of the central trunk and branch structures are analyzed and extracted to obtain feature information;

[0129] A digital map of the human body is generated based on the central trunk and branch structure and the feature information.

[0130] Figure 4 The diagram shows a structural schematic of an embodiment of the electronic device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the electronic device.

[0131] like Figure 4 As shown, the electronic device may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0132] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other network elements such as clients or other servers. The processor 402 executes program 410, specifically performing the following steps in the above embodiment of the human body digital map construction system for electronic devices:

[0133] By using a trained deep learning model or traditional image processing algorithm to segment CT images of the brain, spatial structural information of sinus cavities within the brain can be obtained.

[0134] Based on the spatial structure information of the sinus cavities obtained from the segmentation, a ball-and-stick model is constructed with the sinus cavities as balls and the channels between the sinus cavities as sticks.

[0135] Based on the characteristics of the ball and stick in the ball-and-stick model, the patency between the sinus cavities is analyzed.

[0136] Specifically, program 410 may include program code that includes computer operation instructions.

[0137] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The airborne image processing board includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0138] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0139] Specifically, program 410 can be used to cause processor 402 to perform the operations corresponding to the above-described human body digital map construction system embodiment:

[0140] To obtain CT or MRI images of the dendritic structures within the human body;

[0141] The image is segmented based on computer graphics and tree topology modeling algorithms and / or deep learning algorithms to obtain a tree-like target structure;

[0142] Extract the central trunk and branch structures from the tree-like target structure;

[0143] The features of the central trunk and branch structures are analyzed and extracted to obtain feature information;

[0144] A digital map of the human body is generated based on the central trunk and branch structure and the feature information.

[0145] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A human body digital map construction system, the system comprising: The data acquisition module is used to acquire CT or MRI images of the tree-like structures inside the human body; The image segmentation module is used to segment the image based on computer graphics and tree topology modeling algorithms and / or deep learning algorithms to obtain a tree-like target structure. The structure extraction module is used to extract the central trunk and branch structures from the tree-like target structure; The feature analysis module is used to analyze and extract the features of the central trunk and branch structures to obtain feature information; The map generation module is used to generate a digital map of the human body based on the central trunk and branch structure and the feature information.

2. The system according to claim 1, characterized in that, The system also includes a data preprocessing module for denoising, grayscale conversion, or enhancement of the acquired images.

3. The system according to claim 1, characterized in that, The image segmentation module is also used for: The image is segmented using a trained convolutional neural network, recurrent neural network, or U-net series network to accurately segment the tree-like target structure of the human body.

4. The system according to claim 1, characterized in that, The structure extraction module is also used for: Based on the segmented tree-like target structure, the centerline of the blood vessel or nerve is determined, and the central trunk and branch structure are extracted from the tree-like target structure using distance transformation and skeletonization algorithms.

5. The system according to claim 1, characterized in that, The feature analysis module is also used for: The diameter of the central trunk and branch structures at different locations, the length of each branch, the bifurcation angle, the width and height of non-circular structures, or the number of branches are measured.

6. The system according to claim 1, characterized in that, The system also includes a digital encoding module for digitally encoding and naming the trunk, branches, and nodes in the central trunk and branch structure.

7. The system according to any one of claims 1-6, characterized in that, The digital map of the human body generated by the map generation module specifically includes at least one of the following: Human neural map; Map showing the distribution of the trachea and bronchi in the lungs; Map showing the distribution of major arteries and veins in the lungs; Diagram showing the distribution of blood vessels within the pulp chamber; Distribution and morphology of the pulp cavity; Distribution diagram of the entire dentition; Morphology and distribution of oral and craniofacial bone tissue; Map showing the course of lymphatic vessels and distribution of lymph nodes in the human body; Morphological features of the occlusal surface of a tooth; Diagram of the pathways of the mandibular nerve canal and blood vessels; Diagram of the distribution and course of intestinal blood vessels; Map showing the distribution of cerebral vascular branches.

8. The system according to any one of claims 1-6, characterized in that, The system has at least one of the following uses: It is used to diagnose vascular and neurological diseases by observing abnormal structures in the human body digital map. This helps doctors understand the distribution and direction of blood vessels or nerves in a patient's digital body map, enabling them to develop more precise surgical plans and reduce surgical risks. Used in interventional treatment procedures, the digital map of the human body provides doctors with real-time vascular navigation, guiding them to accurately place catheters. Used in the drug development process, the effects of drugs on the vascular system are assessed based on the aforementioned human digital map; Used in rehabilitation medicine, it helps doctors assess the recovery of patients' vascular function and develop personalized rehabilitation plans; Used for the development of digital design and planning guidance indicators for robotic surgical navigation and path planning; Used in medical education and health management, it can enhance visual memory or clinical teaching by presenting two-dimensional or three-dimensional neural or vascular flow, or help develop health management programs.

9. An electronic device, comprising: processor; and memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the following operations: To obtain CT or MRI images of the dendritic structures within the human body; The image is segmented based on computer graphics and tree topology modeling algorithms and / or deep learning algorithms to obtain a tree-like target structure; Extract the central trunk and branch structure from the tree-like target structure; The features of the central trunk and branch structures are analyzed and extracted to obtain feature information; A digital map of the human body is generated based on the central trunk and branch structure and the feature information.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which, when executed by a processor, perform the following steps: To obtain CT or MRI images of the dendritic structures within the human body; The image is segmented based on computer graphics and tree topology modeling algorithms and / or deep learning algorithms to obtain a tree-like target structure; Extract the central trunk and branch structure from the tree-like target structure; The features of the central trunk and branch structures are analyzed and extracted to obtain feature information; A digital map of the human body is generated based on the central trunk and branch structure and the feature information.

Images