1738 results about "Circumscribed lesion" patented technology

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 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 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 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 discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention provides an index construction method for a medical ultrasonic database, and relates to the technical field of ultrasonic data processing, and the method comprises the steps: reading a multi-frame image sequence from a DICOM ultrasonic database, and generating a diagnosis intention vector for each frame; analyzing the diagnosis report, and segmenting the report into a plurality of segments; lesion detection and segmentation are performed on each frame of image to obtain lesion information, and a cross-frame aggregation strategy is adopted to identify a unified lesion object; extracting a feature vector of each focus object to obtain a focus object feature vector; establishing an alignment mapping between the focus object and the report fragment by adopting triple constraints, and generating a focus report mapping table; and constructing a multi-layer index structure, and storing the focus report mapping table and the multi-layer index structure in parallel. According to the method, a multi-layer index structure comprising a metadata index, a text index, a focus object vector index and a cross-modal embedding index is formed, so that efficient organization and focus-level accurate retrieval of medical ultrasonic data are realized.

The invention relates to a disease diagnosis and treatment method, system and device based on ear-nose-throat endoscope images and a medium, and the method comprises the steps: synchronously collecting dual-spectrum images through time sequence triggering, and solving the problem of shielding of an anatomical structure caused by mucus flow; a dynamic mucus displacement field is modeled through pixel gradient, and misjudgment of a traditional segmentation method on static lesions and dynamic secretions is eliminated; a deformable convolutional layer is adopted to correct the spatial offset of white light and a narrow-band image, and the mismatch of a multi-mode characteristic due to optical scattering is overcome; and finally, a real-time surgical navigation mark and a clinical treatment scheme are synchronously generated based on topological attributes of the focus probability graph, and a closed-loop link from image analysis to diagnosis and treatment decision is realized. According to the method, the functions of mucus interference suppression, cross-modal accurate registration and real-time diagnosis and treatment assistance are integrated in a breakthrough manner, and the focus recognition accuracy and clinical operation efficiency of the endoscope image are remarkably improved.

The invention belongs to the field of medical image processing, relates to a medical image focus segmentation and three-dimensional reconstruction method and system based on artificial intelligence, and aims to solve the problem of low model precision caused by mutual isolation of segmentation and reconstruction and unidirectional transmission of errors. The method comprises the following steps: fusing a multi-modal medical image; a segmentation network is adopted to generate a preliminary focus mask; constructing an initial three-dimensional geometric surface based on the mask, and performing physically-driven curved surface optimization; reversely projecting the optimization model to the feature space of the segmentation network, calculating the spatial inconsistency between the optimization model and network prediction, and generating an attention weight map; feeding back the attention weight map to the segmentation network, and iteratively updating network parameters to refine segmentation boundaries; and based on the final segmentation result after convergence, three-dimensional reconstruction guided by the network features is executed again. According to the method, a closed-loop feedback and collaborative optimization mechanism between segmentation and reconstruction is constructed, and the accuracy of focus segmentation and the geometric fidelity of a three-dimensional reconstruction model are remarkably improved.

The invention discloses an intelligent medical image diagnosis system and method based on hierarchical cross-modal conversion and dynamic feature tracking. The system adopts three-step cross-modal conversion: a first-layer small model for converting user questions to realize medical ontology matching; the second-layer multi-modal model extracts image features, and outputs text states such as JSON data with focus coordinates, density and other features; and the third-layer large model fuses the medical history and the image features to generate diagnosis suggestions, and credibility verification is carried out. A dynamic focus tracking engine is introduced, a focus evolution rule of multiple scanning is analyzed through a convolutional network, and an optical flow field is adopted to compensate artifacts. The system also integrates a multi-expert voting mechanism to simulate a clinical consultation process, and outputs consensus diagnosis and objection viewpoints. A hierarchical routing algorithm is designed for emergency treatment scenes, so that the recognition response time of emergencies such as pneumothorax is shortened. Further, the system automatically generates a full chain of evidence report that conforms to medical regulations, including a model version, a guide reference, and a data hash value.

The invention discloses a CT image analysis method and system based on a neural network, and relates to the technical field of CT image analys.The method comprises the steps that an original CT image is obtained after user authorization, a Laplace operator is adopted to strengthen a focus boundary, and a circular region of interest is intercepted to remove edge sensitive information; extracting edge and texture information in the standardized image; focus area features are focused step by step; executing characteristic distillation balance based on category sample distribution, and outputting a focus characteristic graph with local perception enhancement and sample balance characteristics; segmenting the lesion feature map into serialized units, embedding position codes, inputting the serialized units into a plurality of layers of encoders, and fusing an image structure and text indication information through a dynamic adjustment mechanism; performing linear classification on the global semantic vector to output a diagnosis result, generating a focus thermodynamic diagram, and superposing the focus thermodynamic diagram to an original image for visualization; and performing dynamic optimization based on doctor feedback. The accuracy of feature analysis is improved; the overall operation efficiency of the system is improved.

The invention relates to the technical field of image analysis, in particular to a temporal bone and gallbladder sebaceous tumor detection method and system based on multi-modal medical image fusion. The method comprises the following steps: acquiring a multi-modal temporal bone and gallbladder sebaceous tumor image of a patient, carrying out multi-modal image time sequence space alignment, carrying out feature vector change rate fitting, and constructing a multi-time-point image feature vector map; performing multi-modal image voxel decomposition on the multi-time-point image feature vector map, and performing three-dimensional morphological analysis to generate a geometric morphological parameter set; carrying out potential pathological hierarchical structure mining based on the geometric morphology parameter set, carrying out semantic label labeling, and constructing a multi-modal lesion semantic labeling image; and carrying out multi-time-point image change analysis on the multi-modal lesion semantic marking image, and carrying out parallel lesion evolution state prediction so as to generate a cholangioma evolution state prediction map. According to the method, accurate positioning, effective segmentation and visualized display of the temporal bone and gallbladder sebaceous tumor focus are realized.

The invention belongs to the technical field of medical equipment. The endoscopic surgery operation quality real-time quantitative evaluation system comprises an endoscopic camera probe used for collecting endoscopic images and an endoscopic handle used for operation. The displacement sensor is integrated on the endoscope camera probe and is used for detecting the endoscope entering speed; the pressure sensor is integrated on the gas injection pipeline and is used for detecting the intracavity pressure; the force sensor is integrated on the endoscope handle and is used for detecting the tissue traction deformation; the optical coherence layer scanning probe is integrated on the endoscope camera probe and used for detecting the thickness of the submucous membrane, and the processing terminal is used for conducting real-time quantitative evaluation on the endoscopic surgery operation quality according to the endoscope entering speed, the intracavity pressure, the thickness of the submucous membrane, the tissue traction deformation image, the included angle between an instrument and a focus normal and the complication response time. According to the invention, full-process datamation real-time accurate evaluation of operation skills and operation safety of operators is realized.

The invention relates to the technical field of image processing, and discloses a pulmonary embolism focus segmentation method and system based on image recognition, and a storage medium. The method comprises the following steps: extracting a multi-level blood vessel topological structure of a CTPA image through blood vessel diameter gradient analysis; modeling blood vessel density distribution by using a Weibull mixed model to obtain embolism characteristic parameters; the pixel embolism probability is estimated through variational Bayesian reasoning, and a focus distribution diagram is generated; performing multi-scale feature fusion on the lesion probability graph to obtain a segmentation boundary; and obtaining a final embolism focus segmentation result based on the vascular connectivity constraint optimization boundary. The problems that blood vessel level differentiation processing cannot be achieved, and accurate probability modeling and anatomical constraint verification are lacked are solved. The accuracy of pulmonary embolism focus segmentation is improved.

The invention belongs to the field of characterization learning, and particularly relates to a prostate cancer diagnosis method based on a multimodal large model prompt learning mechanism. Comprising the following steps: step 1, data preprocessing; 2, key frames and similarity are calculated, and irrelevant editing is compressed; step 3, text and image alignment training; step 4, a test stage; according to the method, a similarity-based screening mechanism is provided, ultrasonic videos under coarse-grained labels are preliminarily screened under segmentation of a large model, and focus areas are focused on in time sequence; meanwhile, a pre-processing mechanism based on a large model is provided, and the influence of a large amount of irrelevant information existing in prostate ultrasonic image scanning on the model is compressed at a data end, so that the diagnosis effect of the model is improved.

The invention provides a bone tumor fine-grained classification model training and classification method and device, and the method comprises the steps: constructing a multi-modal positive sample containing global / local positive lateral X-ray images, lesion attributes and patient information, and removing a false negative part in combination with text semantic similarity to construct a high-quality negative sample; global / local image features are extracted through double image encoders, lesion attribute keywords are converted into'entity-translation-existence 'triples based on a medical knowledge base, and basic information of a patient and global / local semantic features of lesion attributes are extracted through a text encoder; infoNCE contrast loss is constructed for global images and global semantics based on contrast learning, global image-text feature alignment and local image-text feature alignment are realized in combination with a local mutual information loss and classification loss training model calculated based on a DV variational formula, medical term semantics are deeply combined, the training stability is improved, and the training efficiency is improved. The accuracy and robustness of bone tumor subtype classification are remarkably improved, and reliable support is provided for clinical precise diagnosis.

The invention belongs to the technical field of medical image analysis, and relates to a lesion detection method and system based on multi-scan interactive deformable Mama, and the method comprises the steps: 1, image block embedding; 2, multi-scale feature extraction and interaction in the backbone network are scanned in parallel; 3, feature pyramid optimization based on dynamic weighted scanning fusion; 4, performing multi-scale fusion; according to the invention, through the adaptive scanning network and the deformable scanning mechanism, the characterization problem of the morphological heterogeneity of the oral cancer focus and the pulmonary nodule microstructure feature is effectively solved, the double breakthrough of the detection precision and the calculation efficiency is realized, and an efficient and reliable solution is provided for multi-cancer medical image analysis.

The invention discloses an efficient medical image segmentation method considering global modeling and local enhancement, and relates to the technical field of image segmentation. According to the method, adaptive space shift operation is executed in different directions through the AS-MLP module, the long-range dependence modeling capability is effectively enhanced, and the recognition performance of a complex structure focus is improved; the channel and space double attention mechanism and multi-scale convolution of the LMCAM module are combined, so that fine-grained feature extraction is realized, and the segmentation precision of the lesion boundary and the small-scale structure is remarkably improved; a lightweight network design is adopted, the calculation complexity is low, the reasoning speed is high, and the method is suitable for resource-limited clinical terminals and real-time diagnosis application; besides, the method has good cross-modal adaptability, can keep stable and efficient segmentation performance in various medical imaging modalities such as CT, MRI, ultrasound and dermatoscope, and has wide application value.

The invention relates to the field of endoscope image processing, and particularly discloses an automatic lesion recognition system for endoscopy of the digestive system department, which is characterized in that after a preprocessed original endoscope image is acquired, a double-branch parallel processing architecture is used to acquire characteristics with low resolution and rich semantic information through a deep context branch, and the characteristics of the original endoscope image are acquired. The potential area of the focus is accurately deduced; meanwhile, the fine texture of the mucous membrane is captured in a lossless manner through shallow detail branches which keep high resolution in the whole process. Furthermore, through a context-guided asymmetric enhancement mechanism, a global view of a deep branch is utilized to generate an uncertainty perception attention map as a reference, and weak detail features corresponding to a potential focus area in a shallow branch are accurately irradiated and adaptively enhanced. Thus, a conservative enhancement strategy is adopted in an uncertain focus area, background noise is effectively inhibited, and therefore the detection sensitivity and robustness of low-contrast and flat focuses are fundamentally improved.

The invention discloses a cardiovascular lesion classification method and system based on data analysis, relates to the technical field of cardiovascular lesion classification, and aims to solve the problem of poor accuracy when cardiovascular lesions of patients are classified. According to the method, multi-dimensional features are extracted for images, time sequence signals and structured data, and fusion is realized through data layer clinical association screening, feature layer composite vector recombination and decision layer weight summation. The method breaks through the limitation of a single data dimension, enables a classification result to be more fit with a pathological mechanism, remarkably improves the recognition precision of complex lesions and complications, guarantees the reliability of hardware through precise configuration of equipment and multi-dimensional detection, customizes a preprocessing process according to the data type, and associates multi-source data through a patient ID and a timestamp, thereby improving the recognition precision of the complex lesions and complications. The problems that equipment is disordered and data formats are different in a traditional process are solved.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention relates to the technical field of medical image information processing, and discloses a medical image information management system based on wisdom medical treatment, which comprises a scene perception preprocessing module, a cross-modal attention fusion module, a weak supervision annotation module, a comparative analysis module, a collaborative report generation module, a knowledge graph reasoning decision module and a consultation collaborative platform module. The image quality is improved through scene adaptive preprocessing; a hierarchical cross-modal attention mechanism is adopted to realize multi-modal feature alignment and fusion; lesion labeling is carried out based on a cascade weak supervised learning strategy; obtaining a focus evolution trend through time sequence correlation analysis; generating a standardized diagnosis report by using a visual language collaborative network; providing treatment scheme recommendation based on individualized knowledge graph reasoning; and a multidisciplinary consultation cooperation platform is constructed. According to the method, the scene adaptability of image quality evaluation is improved, the multi-modal semantic alignment capability is enhanced, the dependence of focus labeling on a fine sample is reduced, and dynamic disease monitoring and individualized diagnosis and treatment decision support are realized.

The invention relates to the technical field of artificial intelligence, and provides a gastric cancer risk quantitative scoring method, system and equipment based on artificial intelligence, and the method comprises the steps: recognizing the image type of each image frame in alimentary canal endoscope image data; for the electronic dyeing image frame, identifying a part contour region and an intestinal contour region of the feature part in the image frame, determining an intestinal epithelial metaplasia grading category of the feature part according to an area proportion of the intestinal contour region in the part contour region, and performing electronic dyeing intestinal scoring on the image data; for the white light image frame, gastroscope part types included in the image frame and focus area types of all gastroscope parts are recognized; performing atrophy scoring on the image data according to the position distribution of the focus area of the atrophy type; performing table state scoring on the image data according to whether the plica enlargement, nodular gastritis and diffuse redness focus areas exist or not; and realizing gastric cancer risk quantitative scoring according to the electronic staining intestinal scoring, the atrophy scoring and the epistatic state scoring.

The invention discloses a preparation method of artemisia apiacea extracellular vesicles, which comprises the following steps: step 1, pre-treating artemisia apiacea, carrying out primary centrifugal treatment, removing large plant tissues and cell debris, carrying out secondary centrifugal treatment, filtering by a needle filter, and collecting; step 2, transferring the filtered suspension to a sucrose density gradient solution, then carrying out third centrifugal treatment, collecting the solution with the concentration of 30% in the layer, and carrying out fourth centrifugal treatment to obtain artemisia apiacea extracellular vesicles; the invention further discloses an artemisia apiacea extracellular vesicle, a pharmaceutical composition for treating cerebral apoplexy, a medicine and application of the artemisia apiacea extracellular vesicle in preparation of the medicine for treating cerebral apoplexy. The naturally-sourced vesicle delivery system provided by the invention is expected to have higher safety and lower toxic and side effects, and the enrichment concentration of the therapeutic component at the focus part of cerebral apoplexy is improved, so that the bioavailability and the treatment efficiency of the medicine are remarkably improved.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

A medical device may include an elongated body having a distal elongated body portion and a central longitudinal axis. The medical device may include a balloon positioned along the distal elongated body portion. The balloon may be configured to receive a fluid to inflate the balloon such that an exterior balloon surface contacts a calcified lesion within a patient's vasculature. The medical device may include one or more pressure wave emitters positioned along the central longitudinal axis of the elongated body. The one or more pressure wave emitters may be configured to propagate at least one pressure wave through the fluid to fragment the calcified lesion. At least one pressure wave emitter may include an optical fiber configured to transmit laser energy into the balloon. The laser energy may be configured to create a cavitation bubble in the fluid.

The invention discloses a breast surgery puncture positioning system, which relates to the technical field of image positioning, and is characterized in that a preoperative medical image and an intraoperative ultrasonic image of a patient are acquired to generate a three-dimensional model, an optimal puncture path is planned by receiving a needle insertion point of a user, and the spatial pose of a puncture needle is tracked; the optimal puncture path and the position and posture of the puncture needle are displayed on the real-time ultrasonic image in an overlapped mode, real-time deviation is calculated, the puncture needle is guided and adjusted to the target focus, and real-time compensation is carried out based on the respiratory signal and / or the displacement of the reference datum. Through the multi-modal image fusion and real-time dynamic tracking technology, the puncture precision and hit rate are remarkably improved, meanwhile, the system intelligently plans a safe path and provides an AR navigation interface, the operation difficulty and dependence on doctor experience are greatly reduced, physiological displacement interference is overcome through respiratory gating and a motion compensation mechanism, and the system has the advantages of being high in accuracy and high in reliability. Accuracy and reliability in the whole operation process are guaranteed, and finally standardized and safe mammary gland puncture diagnosis and treatment operation is achieved.

The invention relates to the technical field of image segmentation, in particular to a tumor prediction method and system based on image segmentation processing, and the method comprises the following steps: dividing a lesion image into sub-regions according to space, extracting probability construction mapping, generating a prediction map, analyzing gradient change, judging a difference region, generating a density mutation map, extracting a gray scale trend, and generating a time consistency mapping table. And fusing the inter-frame probability reconstruction distribution to generate a high-sensitivity image, and aggregating a high-value region to extract a boundary index and mark the boundary index as a suspicious lesion position list. According to the method, the image sequence is divided into the sub-regions, and the prediction map is constructed in combination with the pixel probability and position relationship, so that the region recognition accuracy is improved, the probability gradient in the region is extracted to recognize the abnormal density mutation region, the time gray change direction is tracked, and dynamic consistency classification is realized; adjacent image pixel prediction results are fused to reconstruct local probability distribution, prediction stability is enhanced, abnormal areas are aggregated in the high-sensitivity image, boundaries are marked, and recognition sensitivity and reliability are improved.