2706 results about "Circumscribed lesion" patented technology

The invention discloses an MRI medical image correction method and system based on a convolutional neural network and a computer readable storage medium, and the method comprises the steps: obtaining a brain MRI medical image, and carrying out the preprocessing; extracting lesion features of the brain lacuna infarction from the preprocessed MRI medical image through a convolutional neural network, and positioning a lesion area of the brain lacuna infarction; performing detail enhancement on the lesion area of the cerebral lacuna infarction, segmenting the lesion area and optimizing a segmentation result; and carrying out artifact restoration on the segmented image, and generating a three-dimensional focus visualization model based on the restored image. According to the method, through processing in multiple aspects such as quality evaluation, lesion recognition, segmentation, artifact repair and three-dimensional visualization of the MRI image, the lesion area can be recognized more efficiently and accurately, the image quality is improved, and an efficient and accurate brain lacuna infarction diagnosis auxiliary tool is provided.

The invention discloses an image lesion recognition system for assisting a doctor in the digestive system department in sampling inspection. The image lesion recognition system comprises an image acquisition module, a preprocessing module, an AI lesion recognition module, a feature analysis module, a sampling recommendation module, a data storage module, a real-time interaction module, a system control center, a disinfection monitoring module and a power consumption optimization module. The system integrates white light, narrow-band light and fluorescent endoscope images based on a multi-modal large model, accurately identifies focuses such as polyps and ulcers in real time and marks boundaries, quantitatively analyzes the forms, infiltration depths and texture features of the focuses in combination with super-pixel segmentation and three-dimensional reconstruction technologies, and significantly improves the diagnosis objectivity. According to the system, a sampling scheme matched with a clinical guide is dynamically generated by relying on a reinforcement learning strategy and biomechanical simulation, a focus three-dimensional model and a real-time endoscope picture space are subjected to registration through an augmented reality technology, an operation path is guided by means of tactile feedback, and the accuracy and efficiency of doctor operation are greatly optimized.

The invention relates to the technical field of medical image processing, in particular to a medical image automatic analysis system and method, and the system comprises a time sequence dynamic monitoring module, a lesion area weighted fusion module, a boundary energy optimization module, a multi-channel filtering processing module, and a time sequence feature construction module. According to the method, time sequence tracking analysis of medical images is realized through time sequence dynamic monitoring, change salient region screening and precise lesion evolution trend positioning, lesion data at different moments are integrated by means of weighted fusion processing, the accuracy of time sequence analysis is improved, data fluctuation errors are reduced, lesion boundaries are optimized in combination with gradient analysis and gray scale comparison, and the accuracy of medical image analysis is improved. Noise interference is eliminated, lesion contours are clearer, different frequency features of images are screened through multichannel filtering, lesion differentiation information is highlighted, irrelevant interference is reduced, feature expression is enhanced, a complete lesion time sequence analysis framework is constructed, and a comprehensive image analysis basis is provided for precise medical treatment.

The invention relates to a lung focus medical image segmentation method based on graphics and text information and knowledge embedding. The method comprises the following steps: acquiring a lung medical image of a patient and a corresponding clinical diagnosis report; preprocessing the lung medical image to obtain an enhanced image; inputting the lung medical image and the clinical diagnosis report into the medical visual language model to obtain a focus prompt embedding vector; and inputting the lung medical image, the enhanced image and the focus prompt embedding vector into the medical image segmentation model to obtain a lung focus region segmentation image. By adopting the method, the lung focus can be quickly positioned by the segmentation model through the focus prompt embedding vector, the interference of a non-target area is reduced, and the segmentation accuracy and the target concentration are improved.

The invention relates to the field of artificial intelligence, in particular to a medical image computer-aided analysis method based on deep learning, and aims to solve the problems that an existing medical image analysis method is low in high-resolution image processing efficiency, insufficient in tiny focus recognition precision, weak in model generalization ability and insufficient in multi-modal image fusion. According to the method, a lightweight multi-scale feature extraction network is constructed to improve the high-resolution image processing efficiency, a fine-grained lesion recognition module is introduced to improve the detection precision of a tiny lesion, and a self-adaptive regularization strategy is adopted to enhance the model generalization ability. And a multi-modal deep fusion mechanism is designed to make full use of complementary information of different modal images. According to the invention, medical image analysis which is more efficient, more accurate, higher in generalization ability and capable of effectively fusing multi-modal information can be realized, so that clinical application of deep learning in the field of medical images is promoted.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

The invention discloses a medical image segmentation method and system based on guidance information and a multi-dimensional attention mechanism. The method comprises the following steps: collecting an original dermatoscope image for preprocessing; constructing a segmentation model, wherein the segmentation model comprises a double-path image encoder, a guide information encoder and a mask decoder; the two-way image encoder is used for extracting local detail features and global context semantic information in the image; the guide information encoder is used for converting a coarse segmentation mask predicted by the last round of network into guide feature information; the mask decoder fuses the image feature information and the guide feature information, gradually restores and refines the coarse-grained feature map, and finally outputs an accurate lesion segmentation mask; constructing a loss function, and training the segmentation model by using the preprocessed data; and inputting a to-be-segmented original dermatoscope image into the trained segmentation model, and outputting a lesion region segmentation mask of the image. According to the method, the segmentation precision and the model generalization ability can be improved, and the multi-scale lesion processing ability is enhanced.

The invention discloses an automatic segmentation method and device for a focus area in an ultrasonic image, a medium and a product, and relates to the field of intelligent medical treatment. The method comprises the following steps: firstly, acquiring an original ultrasonic image shot at a lesion part of a patient and preprocessing the original ultrasonic image, then labeling the preprocessed ultrasonic image, and storing the labeled image and the ultrasonic image in a one-to-one correspondence manner to form a sample data set; performing coarse segmentation on a foreground and a background in the ultrasonic image through an automatic threshold segmentation method to obtain a binary mask of the foreground as a coarse mask; the rough mask is converted into multi-mode cue word information; training an improved SAM large model by using the sample data set and the multi-modal cue word information, and using the improved SAM large model as a focus region segmentation model after training is completed; the to-be-segmented ultrasonic image and the cue word information thereof are obtained and input into the lesion region segmentation model, so that the lesion region can be automatically segmented, and the efficiency, accuracy and intelligent level of segmentation of the lesion region in the ultrasonic image are remarkably improved.

The invention relates to the field of medical image processing, in particular to a lung cancer PET-CT fusion segmentation method and system based on multi-modal feature comparative learning, and the method comprises the steps: firstly extracting PET and CT image features, projecting the features to a shared semantic space through a semantic guide type symmetric comparative learning architecture, obtaining key region features through a focus adaptive attention sampling mechanism, and carrying out the segmentation of a target region; optimizing feature representation through a cross-modal feature difference self-calibration mechanism, constructing a multi-scale feature pyramid, fusing features of different scales by using a multi-scale hierarchical contrast learning mechanism, and performing self-supervised learning by combining an anatomical guidance self-supervised contrast learning enhancement module and using a CT anatomical structure, so as to further reinforce the features; a high-precision lung cancer lesion segmentation result is generated through a decoder network, the Dice coefficient is increased from 0.78 to 0.91, and the detection rate of lesions below 10 mm is increased from 65% to 87%. A novel efficient and accurate image processing method is provided for lung cancer diagnosis.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention relates to the technical field of medical image processing, in particular to a multimodal-based digestive tract tumor lesion image segmentation method and segmentation system. The method comprises the following steps: acquiring an alimentary canal tumor lesion image, and extracting an alimentary canal tumor ultrasonic image; evaluating the tumor invasion depth based on the digestive tract tumor ultrasonic image; performing focus three-dimensional visual modeling according to the tumor invasion depth to obtain a digestive tract tumor focus model; extracting a tumor tissue pathological image according to the digestive tract tumor lesion image; performing tumor region segmentation based on the tumor tissue pathological image to obtain digestive tract tumor region data; performing tissue arrangement anomaly detection based on the digestive tract tumor region data to obtain tissue arrangement anomaly data; and calculating a tissue arrangement disorder index according to the tissue arrangement abnormal data and the digestive tract tumor area data. According to the invention, the tumor identification accuracy and the malignant region segmentation precision are improved based on the medical image processing technology.

The invention relates to a small focus segmentation and feature learning method based on Transform and CNN fusion. The method comprises the following steps: preprocessing an input three-dimensional image to obtain original input features, and inputting the original input features into a segmentation network model of an encoder-bottleneck-decoder structure; after passing through an encoder based on channel-Transform fusion, obtaining fusion features through a bottleneck used for expanding a receptive field; wherein the encoder comprises a channel convolution module for local feature capture and a local attention mechanism module for global modeling; the preprocessed input features pass through a channel convolution module and a local attention mechanism module in sequence, and then pass through a bottleneck containing cavity convolution to carry out feature fusion; and inputting the fused features into a decoder based on a jump connection mechanism to gradually restore the low-resolution features to the same size as the input image, and generating a final segmentation result or classification result. According to the method, efficient feature extraction and fine prediction of three-dimensional images such as medical images can be realized.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention relates to the technical field of medical image processing, and particularly discloses a lung focus recognition method and system based on image deep learning, and the method comprises the steps: carrying out the enhancement and registration of an input lung image, and constructing a marking data set; constructing a convolutional neural network model with a multi-scale receptive field, introducing an attention mechanism to focus the model on a lesion area, coarsely and finely identifying the lesion position and estimating the size of the lesion position; further purifying a focus area and removing artifacts and noise by applying morphological filtering and connected domain analysis; and carrying out uncertainty evaluation on the identification result to obtain a focus identification result. According to the method, local details and global structure information in the lung image can be comprehensively captured, the model can be more accurately focused on the lesion area in combination with an attention mechanism, and the lesion recognition accuracy is improved. According to the invention, confidence information of an identification result is provided for doctors. And for a low-confidence result, measures such as manual judgment or re-collected data identification can be taken, so that the diagnosis reliability is improved.

The invention discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

The invention relates to a radiographic image quality evaluation and enhancement method and system based on deep learning. The method comprises the following steps: constructing a multi-modal radiographic image quality evaluation data set containing noise types, artifact levels and diagnostic availability labels; designing a double-branch deep neural network architecture containing a quality evaluation branch and an image enhancement branch, the quality evaluation branch fusing global and local features to evaluate image quality, and the image enhancement branch realizing local enhancement of a focus area; coordinating a loss function optimization process by adopting a self-adaptive joint training strategy; and deploying an online correction module to iteratively optimize the enhancement parameters. The system comprises an image acquisition interface module, a preprocessing module, a quality evaluation engine module, an intelligent enhancement module, a post-processing module and the like. The technology can accurately evaluate the quality of the radiation image, effectively enhance the image, reduce noise and artifact interference and improve the diagnosis availability, and has important application value in the field of medical image processing.

The invention relates to the technical field of remote medical treatment, in particular to a remote ultrasonic diagnosis support system based on cloud computing, which comprises an echo feature analysis module, a dynamic focus screening module, a case image comparison module, a focus trend classification module and a remote diagnosis support module. According to the method, by analyzing echo signals in a remote ultrasonic image data stream and calculating the echo change rate between image frames, the dynamic characteristic capturing capacity of a lesion area is improved, calculation of a gray gradient change value is combined, a suspected lesion area conforming to a change rate stable interval is screened, lesion shape edge curve contour parameters are utilized, and the dynamic characteristic capturing capacity of the lesion area is improved. The method comprises the following steps: performing matching analysis with an ultrasonic case image database, enhancing the accuracy of lesion category attribution, analyzing the morphological change trend of a lesion area, combining trend stability index calculation, realizing lesion evolution classification, ensuring the reasonability of lesion type division, and marking a progressive lesion area, so that remote diagnosis is more intuitive; and the auxiliary decision-making capability is improved.

The invention discloses a breast cancer immunohistochemical index and typing prediction method and system. The prediction method comprises the following steps: acquiring a breast ultrasound image and a breast magnetic resonance image of a patient; performing preprocessing and quality control on the mammary gland ultrasonic image and the mammary gland magnetic resonance image; performing breast lesion area segmentation by adopting a deep learning model to obtain segmented lesion areas; based on the segmented lesion area, extracting multi-modal radiomics characteristics of the breast ultrasonic image and the breast magnetic resonance image; fusing the multi-modal radiomics characteristics of the breast ultrasound image and the breast magnetic resonance image, constructing a machine learning model, and performing immunohistochemical index prediction to obtain an immunohistochemical index prediction result; and performing breast cancer molecular typing analysis according to the immunohistochemical index prediction result. By fusing the multi-modal image information, the tumor features can be described more comprehensively, and the accuracy of biomarker prediction is improved.

The invention discloses a medical image segmentation method based on uncertainty estimation and multistage distillation, which relates to medical image segmentation, and comprises the following steps: quantifying the prediction uncertainty of a teacher model on an input medical image through an uncertainty estimation module, and generating an uncertainty weight; high-confidence-coefficient pseudo labels are screened based on uncertainty weights, and knowledge of the teacher model is transmitted to the student model in stages through a multi-stage knowledge distillation module; embedding a mixed attention module in the teacher model and the student model, and enhancing feature capture of a focus structure and a boundary in combination with a channel attention mechanism and a space attention mechanism; optimizing the training process of the student model by adopting a composite loss function fusing cross entropy loss, Dice loss and distillation loss based on uncertainty; and applying the trained network to a medical image segmentation scene to complete medical image segmentation based on uncertainty estimation and multistage distillation. According to the invention, the problem of scarcity of high-quality annotation data in medical image segmentation is solved.

The invention provides a digital information processing method for a hospital radiology department, which comprises the following steps: S1, multi-modal image collaborative acquisition and standardization: synchronously acquiring anatomical structure images, functional images and metabolic parameter data of a patient through radiology department imaging equipment, and converting the anatomical structure images, the functional images and the metabolic parameter data into space-time aligned three-dimensional digital matrixes; s2, image quality optimization processing: performing nonlinear contrast enhancement and noise suppression on the original image to improve the signal-to-noise ratio of a target area; s3, dynamic self-adaptive registration: according to the biomechanical characteristics of the organ, fusing the rigid transformation model and the elastic deformation model, and according to the digital information processing method for the hospital radiology department, based on the dynamic registration matrix of the biomechanical model, improving the multi-modal image fusion precision; a deep learning segmentation algorithm fused with morphological constraints improves the focus boundary recognition accuracy; a texture mapping three-dimensional reconstruction technology is mixed, and an anatomical structure and metabolism information are presented at the same time; the invention discloses a structured report automatic generation system based on an attention mechanism.

The invention discloses a lung CT image segmentation method based on Transform and U-Net, and the method comprises the steps: firstly obtaining a pulmonary nodule CT scanning image, carrying out the preprocessing, then constructing a segmentation model MCSTU-Net, enabling the model to extract abundant features through the fusion of a convolutional neural network and a Swin Transform module, and enabling the segmentation model to obtain a segmentation model MCSTU-Net. Enhancing and fusing feature information from different levels through a multiple attention fusion module; dividing the data set into a training set, a test set and a verification set, training and verifying the segmentation model, and finally performing focus segmentation on the pulmonary nodule CT scanning image by using the trained and verified model. According to the model and the method constructed by the invention, lesion tissues in the lung CT image can be segmented more accurately, the effectiveness of each module is verified through an ablation experiment, and doctors are assisted to diagnose and treat lung diseases.

The invention discloses a lesion segmentation lightweight method applied to a mammary gland medical detection image, and relates to the technical field of medical image analysis. The method comprises the following specific steps: (1) acquiring a mammary gland medical detection image data set, performing preprocessing operations such as size standardization and data enhancement on an image, and dividing the image into a training set and a verification set; (2) a lightweight medical image segmentation model of a U-shaped coding and decoding architecture is constructed based on deep learning, an encoder of the model adopts an axial depth separable convolution block, and a decoder integrates a hierarchical scale perception fusion block; and (3) inputting the preprocessed training set image into the model, and training the constructed lightweight segmentation model. And (4) inputting the verification set into the trained model, evaluating segmentation precision through indexes, and adjusting and optimizing hyper-parameters according to a result to obtain a verified model. And (5) carrying out preprocessing such as size normalization and noise suppression on the to-be-segmented breast medical detection image. And (6) inputting the preprocessed image into the verified lightweight model, and outputting a pixel-level focus segmentation result to assist clinical diagnosis. According to the method, the model parameter quantity and computing resource requirements are remarkably reduced through lightweight architecture design, the reasoning speed is increased while the segmentation precision is optimized, the method is suitable for application scenes with limited resources, and efficient technical support is provided for rapid and accurate diagnosis of breast cancer lesions.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention discloses a melanoma lesion area segmentation method based on a CLIP multi-modal fusion network. The method comprises the following steps: 1, constructing an MA-CLIP model; 2, a BLIP language model is finely adjusted through a manually-labeled text-image pair, a large-scale multi-modal data set is constructed, a training set, a test set and a verification set are divided, and preprocessing is carried out; 3, training the MA-CLIP model; 4, evaluating the performance of the MA-CLIP model and optimizing parameters; and 5, inputting a to-be-segmented melanoma clinical image into the trained MA-CLIP model, and outputting a segmentation result. According to the method, the problem of insufficient traditional medical data annotation can be solved, accurate guidance of clinical semantics on image segmentation is realized, and the recognition precision and boundary segmentation capability of a focus in a complex form are improved.

The invention discloses an unsupervised lesion segmentation method based on a large model and pseudo label learning, and belongs to the field of image recognition. The method comprises the following steps of: firstly, generating high-quality classification pseudo labels by adopting a visual-language large model (CLIP), and then, supervising and training an image classification network by using the pseudo labels; in the process, a class activation map (CAM) of the classification network is further extracted, and a data enhancement strategy based on dynamic shielding is introduced, so that the performance of the classification network is further improved. Then, the class activation mapping with higher precision is utilized to generate guidance information for a segmentation large model (SAM), so that a high-quality segmentation pseudo label is obtained. Finally, the invention provides a self-training strategy, and the segmentation network is trained and continuously optimized through the pseudo labels, so that higher segmentation precision and efficiency are achieved.

The invention relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

The invention relates to the technical field of medical image processing and analysis, and discloses a method for identifying and classifying lesions in medical images, which comprises the following steps: acquiring a plurality of medical image data, and constructing a multimode medical image data set containing CT, MRI and PET images; training the multi-mode medical image data set by using a hierarchical attention feature fusion network to generate a focus recognition and classification model; a target medical image is partitioned by a dynamic architecture partitioning algorithm based on image complexity, and analysis is performed by using the focus recognition and classification model to obtain focus features; and classifying the lesion features by using an iterative classification algorithm of Bayesian uncertainty estimation, calculating a classification threshold in combination with a medical expert knowledge base, and outputting a final classification result about the lesion. According to the invention, the accuracy and robustness of identification and analysis are improved, and the accuracy and reliability of a focus classification result are ensured.

The invention relates to an operation auxiliary system and method based on image processing, and the method comprises the steps: obtaining a preoperative medical image of a patient, collecting a multispectral imaging medical image of a surface structure, blood flow distribution and tissue elasticity of an operation region in real time, carrying out the image feature extraction after preprocessing, obtaining a preoperative image feature and an intraoperative multispectral image feature, and carrying out the image feature extraction; constructing a biomechanical organ model, updating tissue deformation parameters of the biomechanical organ model, performing space-time alignment on the intraoperative multispectral image features and the biomechanical organ model, generating a three-dimensional enhancement model including vascular topology, lesion boundary and elastic distribution, and superposing the three-dimensional enhancement model to a real-time surgical field image to obtain a real-time surgical field image. Marking the relative position of the surgical instrument and the risk area, generating an intervention signal when the instrument is close to a critical safety distance threshold value, and performing surgical operation by a doctor under assistance. Multi-dimensional intraoperative information is obtained through multi-spectral imaging to construct a high-precision dynamic biomechanical model, and precise navigation and active intervention are achieved in combination with the augmented reality technology.