57 results about "Tissue segmentation" patented technology

The invention discloses a collaborative optimization method and device for three-dimensional tissue segmentation and registration of a brain nerve image, and the method comprises the steps: S01, constructing a segmentation and registration collaborative model which comprises a shared feature encoder, a segmentation path and a registration path, the segmentation path is used for generating a segmentation probability distribution diagram, and the registration path is used for generating a deformation field; s02, acquiring a training set of the brain three-dimensional magnetic resonance image pair; s03, performing cooperative training on the segmentation and registration cooperative model according to a multi-task cooperative loss function, the loss function including segmentation loss, registration loss and a cooperative regularization term, and the cooperative regularization term modulating a deformation field gradient penalty term by using a multi-scale boundary weight map and a tissue-specific mechanical weight; and S04, receiving an image pair to be registered in real time, and inputting the image pair to be registered into the trained segmentation registration collaborative model to obtain a registration result. According to the method, the calculation efficiency can be remarkably improved while the segmentation and registration precision is ensured.

The invention discloses a weak supervision pathological image tissue segmentation method based on text prompt learning. The method comprises the steps of feature extraction and initial class activation graph generation; using an MCRM module to optimize the initial class activation graph to obtain a refined class activation graph; and aggregating the plurality of refined class activation graphs to form a fused pseudo mask, taking the fused pseudo mask as a supervision signal, training a segmentation model, and after the training is completed, segmenting the new pathological image tissue by using the segmentation model. According to the method, a text prompt learning mechanism is utilized to focus the model on learning high-discrimination features, so that the influence of tissue co-occurrence is reduced. An initial class activation graph is optimized through a multi-mode class activation graph refining module, and the integrity of boundary segmentation is enhanced. Meanwhile, pseudo masks from different network layers are fused to train a segmentation model, and semantic segmentation of the pathological image is realized. According to the method, high-annotation data dependence is effectively relieved, and the generalization ability of the model is improved, so that application in the field of artificial intelligence-assisted medical treatment is promoted.

The invention discloses a high-precision and automatic dental crown generation method. The method comprises seven steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection, and dental crown generation and multi-dimensional refinement. The method comprises the steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection and dental crown generation. Through combination of deep learning and a point cloud algorithm, high-precision segmentation of teeth and gingiva and precise reduction of an occlusion relationship are realized, an oral anatomical feature library and a clinical repair standard are fused, and through multi-dimensional fine adjustment optimization, a dental crown 3D model which is high in fitting degree, harmonious in occlusion and capable of meeting clinical requirements is generated. The method is full-process automatic, greatly improves efficiency, reduces operation threshold, reduces material waste and diagnosis and treatment cost, remarkably improves repair success rate, and is suitable for various oral repair scenes.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention relates to the field of artificial intelligence and medical image processing, in particular to a mammary gland pathology visual model establishing method based on multi-model fusion and combined distillation, which comprises the following steps: performing tissue segmentation and dyeing normalization on a full-slice image; inputting the image blocks into a pre-training teacher model of a plurality of freezing parameters in parallel to extract high-dimensional features, and generating unified enhanced features through a learnable feature fusion network; constructing a student model, and performing end-to-end training by using a joint loss function including feature simulation, logic output distillation and multi-task supervision; and connecting a plurality of task specific prediction heads to the student model, and realizing full-slice-level multi-task diagnosis and treatment prediction through an aggregation strategy. According to the technical scheme, efficient knowledge migration and multi-task cooperation can be achieved, and the accuracy, generalization ability and reasoning efficiency of mammary gland pathology analysis are remarkably improved.

The invention provides a method for constructing a CT-MRI (Computed Tomography-Magnetic Resonance Imaging) personalized three-dimensional heart model based on multi-modal imaging, which comprises the following steps of: firstly, carrying out myocardial and infarction region segmentation on a CMR-LGE image, and carrying out myocardial and intramyocardial adipose tissue segmentation on a CE-CT image to generate a corresponding Label image; then, a high-precision three-dimensional volume mesh model of the corresponding ventricle is constructed based on the segmentation results of the two types of images; and then, mapping the corresponding Label image with the tissue type to a corresponding finite element model according to the coordinates of the grid, and obtaining a corresponding personalized ventricular model with the tissue type. An interpolation method based on a general ventricular coordinate system is adopted, data of an MR ventricular model with infarct tissue attributes is transferred to a CT ventricular model, and therefore a mixed CT-MRI ventricular model is constructed. And finally, storing the constructed CT-MRI ventricular model data with the integrated tissue attributes as a standard geometric and topological file format.

The invention discloses an immediate implantation operation method and system based on augmented reality. The method comprises the steps that CT data of the oral cavity and the head of a patient are acquired and subjected to denoising and normalization processing; the method comprises the following steps: constructing oral digital twins of a patient based on CT data, and completing tissue segmentation, key position marking and gravity center calculation; calculating tooth extraction and implantation paths through operation design software, dynamically predicting stress states of the alveolar bone at different implantation angles and depths based on finite element analysis, and generating an implantation planning scheme; accurately registering the digital twin with the oral cavity of the patient by using multi-point identification and an SLAM (Simultaneous Localization and Mapping) algorithm, and establishing a space mapping relation of an implanting tool; a doctor wears AR glasses to carry out surgery operation, a virtual guiding path and implanting depth and angle information are displayed in real time, and a three-dimensional virtual resistance simulation curved surface is overlaid to prompt bone wall resistance changes; tooth extraction and implantation operations are completed according to AR prompts, stable fusion of the implant and a tooth extraction socket or a bone wall is achieved, and data of the whole operation process are recorded for postoperative analysis.

This invention discloses a kidney pathology image processing method integrating multi-tissue segmentation and quantitative lesion analysis, belonging to the field of medical image processing technology. The method includes the following steps: acquiring and preprocessing PAS-stained whole-slice images of kidney pathology; fine-tuning the segmentation model using an unsupervised domain adaptive strategy to address batch-to-batch staining differences; inputting the preprocessed image into a multi-class semantic segmentation neural network to obtain tissue segmentation results; training the network based on pixel-level annotations, employing a Class-Token mechanism, encoder-decoder architecture, and multi-scale feature fusion, and optimizing the Dice loss and binary cross-entropy loss based on joint weighting of categories and boundaries; performing statistical analysis based on the segmentation results and outputting quantitative analysis results. This invention provides an objective, reproducible, and intelligent auxiliary tool for the accurate assessment and large-scale clinical research of chronic kidney disease.

The present disclosure provides an abnormal tissue growth prediction method, device, electronic equipment and storage medium, the method comprising: obtaining an abnormal tissue growth prediction model; obtaining a historical sample sequence and a target growth duration, the historical sample sequence being arranged in chronological order by M historical samples obtained by examining abnormal tissues; performing a time sequence feature extraction operation; inputting the time sequence features of the historical sample sequence and the Mth historical sample with added noise data into a generative model, outputting post-growth image noise and post-growth abnormal tissue segmentation results; based on a preset denoising formula, using the Mth historical sample with added noise data and post-growth image noise to obtain post-growth image prediction results. In this way, the recurrent neural network for extracting time dimension information is embedded into the generative model for extracting spatial dimension information, improving the performance of the model and making the prediction results of abnormal tissue growth more accurate.

The application provides a multi-modal tissue segmentation method and a surgical navigation system, and the method comprises the following steps: inputting three-dimensional medical images of multiple modes into corresponding deep learning network models respectively for segmentation to obtain corresponding three-dimensional segmentation results; registering the three-dimensional medical images of each mode to obtain a registration relationship; and according to the registration relationship, combining the three-dimensional medical images of each mode to weight and fuse the three-dimensional segmentation results of each mode according to preset weights of each type of tissue. The application trains a segmentation model for each mode of medical image, each deep learning model has stronger segmentation capability for the medical image of the corresponding mode, and has higher segmentation precision; different types of medical images are respectively designed with preset weights (mode weights) of each type of tissue, and the segmentation results are fused according to the mode weights in the fusion process, so that the tissue structure characteristics are more fully reflected, and the segmentation precision is improved.

The invention discloses a sensor array error evaluation method for magnetocardiogram source imaging, which relates to the technical field of biomedical signal analysis, and comprises the following steps: performing tissue segmentation and three-dimensional reconstruction on a CT image to generate a CT model; carrying out registration on the 3D scanning model and the CT model; error types existing in the information of the magnetocardiogram sensor array are set; simulating and generating a magnetocardiogram signal containing error influence; and evaluating the influence of the error on the source imaging performance. The method can systematically and comprehensively quantify the influence of crosstalk, gain error, sensitive axis angle error and position error on the imaging performance of the magnetocardiogram source and three core dimensions of positioning precision to construct a unified multi-dimensional evaluation system, overcomes the defect of lack of comprehensive evaluation standards in the prior art, can also provide priority guidance for error calibration of an MCG system, and improves the accuracy of the MCG system. And the imaging accuracy, the clinical availability and the application value are obviously improved.

The invention discloses a medical image lesion segmentation method and system for a radiology department, and relates to the field of medical images. A bone mask and a bone boundary probability graph are generated by constructing multi-window input of a brain window, a subdural window and a bone window and utilizing a bone tissue segmentation network, and feature suppression processing and boundary enhancement processing are introduced in an encoder stage and a decoding stage respectively so as to weaken the influence of high-density artifacts in a bone region and strengthen boundary expression of a bone attachment region. And further executing probability correction based on a bone mask on the initial hemorrhage probability graph, reducing bone region false detection, and obtaining an accurate cerebral hemorrhage segmentation result. The system comprises an image access module, a multi-window construction module, a bone prior generation module, a cerebral hemorrhage segmentation module, a probability correction module and a clinical quantification module, and can output structured indexes such as hemorrhage volume, mass center and diffusion direction. According to the method, the problems of bone sticking false detection, fuzzy edge, small-size focus missing detection and the like are solved, and the accuracy of automatic cerebral hemorrhage segmentation is remarkably improved.

The application discloses a pericardial fat tissue segmentation method and device based on an MR image, equipment and a medium. The method comprises the following steps: acquiring a target heart magnetic resonance image of a target object containing pericardial fat tissue; segmenting the pericardial fat tissue in the target heart magnetic resonance image based on a tissue segmentation model to obtain the target pericardial fat tissue of the target object; the tissue segmentation model comprises a three-branch cross-domain feature collaborative encoder, a double attention feature fusion module and a dynamic boundary perception decoder; the three-branch cross-domain feature collaborative encoder extracts features from the global, local and frequency domains based on a three-branch structure; the double attention feature fusion module is established based on a spatial attention mechanism and a parallel channel attention mechanism; and the dynamic boundary perception decoder is used to enhance the perception ability of the global and the boundary. The scheme can precisely and efficiently segment the pericardial fat tissue in the heart MR image by using the tissue segmentation model based on multi-feature collaboration.

The invention relates to the technical field of image processing, in particular to a tissue segmentation method based on magnetic resonance imaging, which comprises the following steps of: in an MRI image, respectively segmenting a focus area and a surrounding tissue area by using two groups of U-net networks, and calculating an attention map of the focus area and an attention map of the surrounding tissue area; respectively shielding the focus area and the surrounding tissue area, and correspondingly obtaining an MRI image of focus shielding and an MRI image of surrounding tissue shielding; re-segmenting the focus area and the surrounding tissue area by using the two groups of U-Net networks to obtain enhanced segmentation results of the focus area and the surrounding tissue area; and a lightweight network MobileNet is utilized to construct a lightweight model used for prostate part tissue segmentation. According to the prostate MRI image segmentation method based on the double-U-Net structure, through the combination of an attention mechanism and masking re-segmentation, the segmentation precision of the focus and the surrounding tissue in the prostate MRI image is improved, and through the combination of the double-U-Net structure and lightweight deployment, the segmentation performance and the segmentation efficiency are guaranteed.

A tissue segmentation device, controller, and methods therefore are disclosed. The device has an active electrode, a return electrode, a mechanical force application mechanism, voltage and current sensors, and a controller. The controller has a processing component, configured to assign a circuit status to a circuit comprising the at least one electrode. IF (PF≈0) and ((Vrms / Irms)≥T), THEN the circuit status is “open”. IF (PF≈0) and ((Vrms / Irms)<T), THEN the circuit status is “short”. PF is a power factor of power applied to the electrosurgical device. T is a threshold value.

The invention discloses a metallographic image generation method based on a channel attention mechanism, and the method comprises the steps: employing a deep generative adversarial network as a basic framework, employing a Pix2pixHD multi-scale generator and discriminator structure, embedding a channel attention module (MET-SE) in a middle layer of the generator, and constructing a microstructure segmentation image, and obtaining a metallographic image through employing the deep generative adversarial network. The importance of different channel features is weighted, so that the model adaptively highlights key information of a grain boundary and a second equal microstructure in a feature extraction process, and combined optimization is performed on the model in combination with composite loss functions such as pixel-level reconstruction, adversarial training, perceptual constraint and feature matching. And the detail performance of the grain boundary and the second phase can be better highlighted. The method not only improves the definition and authenticity of the generated image, but also ensures the continuity among different tissues, and effectively solves the problems of insufficient metallographic image generation definition, detail missing and limited data in the prior art.

This invention discloses an intelligent puncture path planning method for interventional surgical robots, belonging to the field of interventional robot path planning technology. The method acquires multimodal medical images of the patient's puncture site, completes organ tissue segmentation and three-dimensional reconstruction, generates a digital twin anatomical model, and calibrates basic puncture parameters; constructs a respiratory phase tracking model, combines the basic puncture parameters to lock the respiratory cycle and real-time respiratory phase, and outputs the three-dimensional predicted coordinates of the puncture target point for a pre-set duration; acquires needle insertion force data through a six-dimensional force sensor, calculates tissue compression deformation and needle body flexible bending offset in real time, and generates a feedforward compensation vector; fuses the three-dimensional predicted coordinates and the feedforward compensation vector to generate a dynamic puncture path, performing continuous needle insertion without interruption throughout the entire process. This invention improves upon the shortcomings of traditional static path planning, which cannot adapt to respiratory displacement and tissue deformation, effectively improving puncture positioning accuracy and is suitable for minimally invasive interventional puncture surgery scenarios.

This disclosure provides an image detection method, apparatus, electronic device, and storage medium. The image detection method includes: acquiring a multiplex immunofluorescence image to be tested, wherein the multiplex immunofluorescence image is imaging data after multiplex immunofluorescence staining of a tissue sample to be tested; performing tissue segmentation based on the multiplex immunofluorescence image to obtain a tissue region image; and performing image sharpness detection on the tissue region image to obtain a detection result. The image detection method provided by this application can effectively eliminate background noise interference in multiplex immunofluorescence images caused by staining at different locations in the image, greatly reducing the proportion of background in the input image for subsequent image detection, and improving the accuracy and reliability of the image detection results.

The current disclosure provides methods and systems to reduce an amount of noise in image data. In one example, a method for creating synthetic computed tomography (CT) images for training a model to reduce an amount of noise in acquired CT images is proposed, comprising performing a tissue segmentation of reference images of an anatomical region of a subject to determine a set of different tissue types of the reference images; and generating synthetic CT images of the reference images by assigning CT image values of the synthetic CT images based on the different tissue types.

The application discloses a pericardial fat tissue image segmentation method, device, equipment and medium. The method comprises the following steps: acquiring a cardiac magnetic resonance image of a target object, wherein the cardiac magnetic resonance image contains pericardial fat tissue; segmenting the pericardial fat tissue in the cardiac magnetic resonance image by using a pre-trained tissue segmentation model to obtain target pericardial fat tissue of the target object; the tissue segmentation model comprises an encoder, a decoder and a spatial channel attention module; the encoder comprises a double-branch collaborative module; the decoder comprises a multi-layer fusion convolution module; the double-branch collaborative module is used for feature extraction based on a double-branch network; the spatial channel attention module is used for feature enhancement in the spatial and channel dimensions; and the multi-layer fusion convolution module is used for feature fusion based on grouped convolution. The scheme can fully mine semantic information and structural features in the image by using the tissue segmentation model, improve the segmentation performance in a complex scene, and efficiently and accurately segment the pericardial fat tissue.

The invention relates to the technical field of medical image analysis, in particular to a preoperative evaluation-oriented pancreatic mesangial region perivascular inflammation imageomics identification method and system. The method comprises the following steps: acquiring a multi-stage enhanced abdomen image, and generating a standardized image structure through data quality control, de-noising registration, intensity normalization and voxel resampling processing; carrying out pancreatic mesangial region blood vessel candidate region positioning, blood vessel skeleton extraction and center line tracking, and constructing an annular region-of-interest structure; performing blood vessel and surrounding tissue segmentation and mask generation, extracting traditional image omics features and coupling features reflecting an interaction relationship between the blood vessel and the surrounding tissue, and generating an effective feature set; and through domain correction, identification model training and parameter solidification, generating a reasoning report containing an inflammation identification tag and interpretability mapping. According to the invention, automatic, accurate and quantitative identification of the perivascular inflammation is realized, and objectivity and accuracy of preoperative evaluation are effectively improved.

The invention provides a dental image segmentation method and system based on a frequency domain enhancement and dynamic scanning mechanism, the method is constructed on a visual state space model framework, and the core comprises a dynamic scanning part and a frequency domain enhancement part, the dynamic scanning block adaptively adjusts a sampling position and a scanning sequence through a trainable offset prediction network, dynamic scanning based on image content is realized, spatial continuity is kept, and the structural characterization capability is improved; frequency domain enhancement is combined with wavelet decomposition and spectrum pooling technologies, high and low frequency characteristics are balanced, and intermediate frequency components are enhanced, so that accurate boundary positioning and structure identification are still kept under unfavorable imaging conditions such as noise, light reflection and uneven illumination. Compared with the existing dental image segmentation method, the method provided by the invention can generate high-quality tooth, gingival and oral cavity tissue segmentation masks. The method can be widely applied to the fields of digital dental diagnosis, treatment planning and intelligent medical image analysis, and has relatively high practical value and popularization prospect.

This application discloses a method, system, device, and medium for transcranial magnetic stimulation (TMS) localization based on electric field guidance, belonging to the field of TMS technology. The method includes: acquiring an individual's local three-dimensional image block to be tested; obtaining an individual predicted local tissue segmentation block based on a segmentation model and the individual's local three-dimensional image block; acquiring the primary electric field distribution of the coil; obtaining a predicted local electric field block based on a local electric field estimation model, the individual predicted local tissue segmentation block, and the primary electric field distribution; constructing a candidate coil pose set based on the scalp projection position of the target point; calculating the average electric field intensity of each candidate coil pose in the gray matter region near the target point based on the predicted local electric field block; determining the candidate coil pose with the largest average electric field intensity as the optimal coil pose; and performing coil localization based on the optimal coil pose. This application can quickly and accurately achieve TMS coil pose localization, improving interpretability.

The application discloses a stroke prediction method and device based on a cerebrovascular image feature group, a medium and a product, relates to the field of image processing, and comprises the following steps: constructing a clinical database; constructing a clinical cause classification prediction model according to multi-modal image feature group data, clinical feature data and patient clinical cause classification; determining cerebrovascular image features by adopting a cerebrovascular tissue segmentation model and voxel-based morphological analysis according to TOF-MRA, and performing positioning diagnosis on a responsible blood vessel area according to the cerebrovascular image features; and constructing a stroke recurrence risk prediction model according to the cerebrovascular image features in the TOF-MRA, SWI, T1WI, T2-FLAIR and corresponding clinical feature data. The application can quickly and accurately realize cause classification of an acute stroke patient in a clinical scene, accurately identify a responsible blood vessel, and realize recurrence risk prediction in an early stage of the disease.

The application relates to a thymus disease image intelligent diagnosis system based on a region attention network, and belongs to the technical field of image analysis and artificial intelligence, the system comprising a CT image preprocessing module, an abnormal tissue detection and positioning module, an abnormal tissue segmentation module and an abnormal tissue classification and thymoma staging module; the CT image preprocessing module is used for denoising and enhancing the CT image; the abnormal tissue detection and positioning module uses a region attention network and a ViT to detect and position the abnormal tissue in the CT image; the abnormal tissue segmentation module automatically segments the abnormal tissue region by means of a U-Net++ network and extracts a key feature region; the abnormal tissue classification and thymoma staging module is used for extracting features of the abnormal tissue region by the ViT, and then simultaneously performing tissue classification and staging tasks. The system can more accurately capture the lesion region features in the thymus disease image, improve the early thymoma detection capability of the system and reduce errors.

The application discloses a high-precision and automatic crown generation method, which comprises seven steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud accurate registration, abutment and related gingival accurate segmentation and modification, multi-tissue segmentation of the environment around the abutment, secondary accurate detection of the neck margin line, crown generation and multi-dimensional fine modification. Through the combination of deep learning and point cloud algorithm, high-precision segmentation of teeth and gums and accurate restoration of occlusion relationship are realized, the oral cavity anatomical feature library and the clinical restoration standard are fused, and the 3D crown model with high adhesion, coordinated occlusion and clinical requirements is generated through multi-dimensional fine tuning and optimization. The method is fully automated, greatly improves the efficiency, reduces the operation threshold, reduces the material waste and the diagnosis and treatment cost, significantly improves the restoration success rate, and is suitable for various oral cavity restoration scenes.

The application discloses a kind of based on eyeball's electromagnetic wave conduction and scattering process calculation method and system, it is related to brain detection technical field, method adopts SegNet segmentation algorithm to realize eyeball tissue segmentation, compared with U-Net, while keeping the precision of anatomical features and dielectric parameters, its dynamic local-global feature interaction mechanism and space information reservation jump connection solve the problem that traditional ignores spatial correlation;In the solution of Maxwell equation, LRBFCM-PSM joint algorithm fuses dynamic hyperparameter optimization and multimodal feature engineering, improves convergence efficiency and calculation accuracy;SAR gradient adaptive grid processing is realized by KD tree algorithm, high gradient area is encrypted, low gradient area is sparse, and precision and calculation amount are balanced;Finally, Monte Carlo photon transport and electromagnetic field data are coupled, accurately simulate the energy absorption of electromagnetic wave in heterogeneous tissue, compared with single physical field simulation, reduce eyeball SAR error.

This invention discloses a collaborative optimization method and apparatus for three-dimensional tissue segmentation and registration of brain neural images. The method includes the following steps: Step S01. Constructing a segmentation and registration collaborative model, including a shared feature encoder, a segmentation path, and a registration path. The segmentation path is used to generate a segmentation probability distribution map, and the registration path is used to generate a deformation field. Step S02. Obtaining a training set of three-dimensional magnetic resonance imaging (MRI) images of the brain. Step S03. Collaboratively training the segmentation and registration collaborative model according to a multi-task collaborative loss function. The loss function includes segmentation loss, registration loss, and a collaborative regularization term. The collaborative regularization term modulates the deformation field gradient penalty term by using a multi-scale boundary weight map and tissue-specific mechanical weights. Step S04. Receiving the image pairs to be registered in real time and inputting them into the trained segmentation and registration collaborative model to obtain the registration result. This invention can significantly improve computational efficiency while ensuring segmentation and registration accuracy.

The invention relates to the technical field of virtual simulation treatment, in particular to an AI-driven virtual simulation treatment training system for oral-maxillofacial war wounds, which comprises an image data processing module for sensing image data, dividing the image data into a plurality of different image groups and preprocessing each image data in the image groups; the tissue segmentation module is used for receiving the preprocessed image group and then identifying all tissues in each piece of image data by adopting a medical image segmentation algorithm, and the medical image segmentation algorithm specifically comprises a coding unit, a dense jump connection unit, a decoding unit and a depth supervision output unit; the war wound model building and calling module is used for converting a two-dimensional skeleton contour into a three-dimensional grid model, namely a war wound model, through voxel division, contour surface extraction and grid generation on the basis of pixel coordinates of the segmentation mask; basic information of the patient is received, and a mapping relation between the war wound model and the image group is established.

The invention relates to the technical field of medical image processing, in particular to a pancreas focus tissue segmentation method and system based on multi-target recognition, and the method comprises the following steps: obtaining a pancreas tissue CT image; extracting a foreground region from the pancreatic tissue CT image, and performing classification identification on the foreground region through a pre-established classification model to obtain an attribute category of the foreground region; and according to the U-Net network corresponding to the attribute category of the foreground region, performing target segmentation on the foreground region to obtain a multi-target segmentation result of the pancreatic tissue. According to the method, through extraction and classification identification of the foreground regions, the corresponding intra-tumor tissue and peritumor tissue segmentation process is carried out on the foreground regions of the corresponding categories, interference of other regions is avoided, and the segmentation efficiency and segmentation precision of the intra-tumor region and the peritumor region are improved.