821 results about "Automated segmentation" patented technology

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.

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 discloses an IG-MambaUNet image segmentation model, a model training method and an application method thereof, the IG-MambaUNet image segmentation model comprises an encoder and a decoder which are symmetrically arranged in parallel, the encoder comprises three first encoding modules and a second encoding module, the three first encoding modules are sequentially connected from top to bottom, the second encoding module is connected with the first encoding module on the bottommost layer, and the third encoding module is connected with the second encoding module on the bottommost layer. The input end of the first coding module on the topmost layer is connected with a first convolution and maximum pooling operation module, the first coding module is formed by coupling an IG-Mama module and a Patch Merging module, the second coding module comprises an IG-Mama module, the IG-Mama module comprises a local feature extraction module, a global feature extraction module and a gating attention module, and the global feature extraction module comprises a local feature extraction module, a global feature extraction module and a gating attention module. The decoder comprises a second coding module and a first coding module which are sequentially connected from bottom to top, and the output end of the first decoding module on the topmost layer is connected with a second convolution and maximum pooling operation module. According to the scheme, fine segmentation is realized, and the method can be suitable for automatic cell body segmentation of histological dyed ganglion images with complex textures and fine structures.

The invention belongs to the technical field of medical image processing, and particularly relates to an ultrasonic mammary gland lesion area automatic segmentation method based on multi-scale hybrid convolution, which comprises the step of carrying out image segmentation by adopting U2-Net as a network framework, and is characterized in that: at a jump connection part in each residual block, a multi-scale hybrid convolution algorithm is adopted to carry out image segmentation; the feature maps are processed through a multi-scale attention gate and then jump connection with the corresponding feature maps is carried out. According to the method, a hybrid convolution block and a multi-scale attention gate are fused with a U2-Net network structure, and unimportant features are further inhibited and valuable features are enhanced through three different receptive field convolution blocks, namely point-by-point convolution, common convolution and expansion convolution, so that a breast cancer lesion area is quickly and effectively segmented; secondly, a hybrid convolution block is connected to the last convolution layer of the encoder of the residual block and is used for extracting global context information of the small U shape; the method can improve the recognition and segmentation efficiency of the lesion area of the ultrasonic image, reduces the workload of a doctor, and assists the clinical diagnosis.

The invention discloses a cervical cancer close-range radiotherapy high-risk target area sketching method fused with a multi-modal image. The method comprises the following steps of collecting an MRI image scanned before radiotherapy and a CT image during radiotherapy of a cervical cancer close-range radiotherapy patient and annotation data of the MRI image and the CT image; the method comprises the following steps: preprocessing an MRI image scanned before radiotherapy and a CT image during radiotherapy, and converting annotation data into a three-dimensional tag image; on the basis of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, registration of the MRI image and the CT image is conducted through a pre-constructed registration neural network model, feature extraction and fusion are conducted on the registered MRI image and the registered CT image through a pre-constructed high-risk target area automatic segmentation neural network model of a multi-scale cross-modal attention mechanism, and the high-risk target area automatic segmentation neural network model of the multi-scale cross-modal attention mechanism is obtained. Automatic delineation of a cervical cancer close-range radiotherapy high-risk target area is realized; according to the method, automatic segmentation of HR-CTV in close-range radiotherapy of cervical cancer is realized, high efficiency, accuracy and generalizability are realized, and intelligent support can be provided for clinical work.

The invention discloses a CT image segmentation method for spine surgery, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a spine CT image of a patient, and carrying out the preprocessing of the spine CT image; adopting a centrum gap detection method to detect a centrum gap region of a spine in the preprocessed CT image, and performing automatic segmentation on each centrum based on centrum gap region information to obtain a preliminary segmentation result of the centrum; extracting local texture features of the intervertebral disc and the nerve root in the intervertebral space area, and classifying the local texture features through a classifier to generate a preliminary segmentation result of the intervertebral disc and the nerve root; performing rigid registration and non-rigid registration on the initial segmentation results of the vertebral body, the intervertebral disc and the nerve root and the standard three-dimensional model, and identifying an abnormal region in segmentation; by extracting local texture features of the intervertebral disc and the nerve root and combining a support vector machine classifier, accurate segmentation of various structures can be realized.

The invention relates to the technical field of medical image processing, and particularly discloses a medical image segmentation method with adaptive receptive field and feature correction, which comprises the following steps: (1) acquiring an original medical image and a segmentation label thereof, and constructing a training and testing data set; (2) carrying out size normalization and enhancement processing on the image; (3) establishing an improved U-shaped encoder-decoder segmentation network, introducing an adaptive branch mixed shape convolution module in a shallow layer, and improving edge and texture feature modeling capability by adopting a multi-branch banded convolution and channel attention mechanism; (4) a residual directional feature interaction module is introduced into a deep layer, a spatial dependency relationship is modeled through an information interaction structure in the horizontal and vertical directions, and the direction sensing ability of the heterostructure is enhanced; and (5) completing network training and reasoning, and outputting a segmentation result. The method gives consideration to the calculation efficiency and the segmentation precision, and is suitable for the automatic segmentation task of various types of medical images with complex structures.

The invention discloses a tooth three-dimensional digital model registration and tooth neck line extraction method based on oral scanning and CBCT, and relates to the field of tooth three-dimensional digital model registration. Comprising the following steps: respectively acquiring three-dimensional oral images of oral scanning and CBCT (cone beam computed tomography), and automatically segmenting three-dimensional structures of dental crowns and teeth by utilizing pre-trained V-Net and ToothNet image segmentation models. Alignment and registration of two modal images are realized through a feature extraction network, an information exchange network and a rigid transformation network. The feature extraction network extracts local and global features by using far-point sampling and a dynamic graph convolutional neural network, the self-attention and cross-attention module performs feature enhancement and information exchange between the same mode and different modes, and based on a registration result, a tooth neck line is further extracted and optimized. According to the method, the tooth three-dimensional digital model can be quickly and accurately registered, a high-precision tooth neck line extraction result is provided, the manpower and time cost of a medical institution can be reduced, and the medical efficiency is improved.

The invention discloses a brain tumor area automatic segmentation method and system based on deep learning, and belongs to the technical field of image segmentation, and the method comprises the following steps: obtaining a disclosed brain tumor segmentation data set, and constructing a multi-modal brain tumor medical image database; performing data preprocessing on the multi-mode brain tumor medical image database; dividing the preprocessed multi-modal brain tumor medical image database into a training set, a verification set and a test set in proportion; a deep learning model LG-FPN-TransBTS based on the improved TransBTS is designed, and the deep learning model LG-FPN-TransBTS is designed; and training, evaluating and applying the model. The system comprises a data preprocessing module, a data set division and secondary processing module, a deep learning model construction module, a model training and optimization module and a model evaluation and application module. According to the method, the precision and robustness of brain tumor image segmentation are remarkably improved.

The invention provides a method for automatically segmenting a pulmonary artery tree and a tracheal tree on a CTPA image, and relates to the technical field of image processing, and the method comprises the steps: carrying out the data preprocessing of a pulmonary artery angiography image, and obtaining a preprocessed image; refining the preprocessed image to obtain a center line and a tubular structure radius; constructing three-dimensional Gaussian distribution according to the center line and the radius of the tubular structure, and obtaining a Gaussian distribution thermodynamic diagram according to the three-dimensional Gaussian distribution; inputting the preprocessed image into a first initial network to obtain an initial prediction result, and training the first initial network through the initial prediction result and a Gaussian distribution thermodynamic diagram to obtain a first segmentation network; inputting the preprocessed image into a first segmentation network to obtain a first segmentation result; and inputting the first segmentation result and the preprocessed image into a second segmentation network to obtain a final segmentation result. According to the method, the blood vessel segmentation technology is optimized through the Gaussian distribution thermodynamic diagram of the center line, and the recognition accuracy and segmentation precision of the model are improved.

The invention provides an aorta morphological characteristic automatic measurement and clinical aid decision-making method and system based on artificial intelligence, and is suitable for three-dimensional medical image analysis of aorta and main branches thereof. The method comprises the steps of image data acquisition and preprocessing, vascular structure automatic segmentation, center line extraction and key point positioning, multi-parameter automatic measurement, risk assessment and clinical aid decision making, data standardization output and system feedback optimization. The corresponding system comprises an image acquisition and processing module, a segmentation module, a center line extraction module, a parameter measurement module, an auxiliary decision-making module, a data output module and a verification optimization module. Through a deep learning model and a morphological calculation means, efficient recognition and quantitative analysis of a vascular structure are realized, and individualized risk suggestions and surgical plans are output based on a statistics and machine learning model. According to the method, the automation, standardization and intelligence levels of blood vessel analysis are improved, and the clinical auxiliary decision-making capability under complex cases is enhanced.

The invention relates to the technical field of image segmentation, in particular to an automatic segmentation method and system for an echocardiogram of the department of cardiology, and the method comprises the following steps: based on image data of the echocardiogram, extracting gray level distribution, edge feature and texture feature information, analyzing gray level change amplitude, screening gray level change abnormal regions, and recognizing connectivity features. According to the method, the segmentation accuracy is effectively improved by extracting image gray, edge and texture features and identifying abnormal regions, noise and artifact interference are reduced by optimizing low connectivity regions, and the segmentation accuracy is improved. A problem area is analyzed and positioned in combination with multi-frame gray level change, a segmentation result is adjusted, the processing stability and consistency are enhanced, meanwhile, an optimized alarm node is output based on a frequency trend, more accurate and stable heart image analysis is supported, and the clinical application practicability is improved.

The invention discloses a urinary calculus CT image automatic segmentation method based on deep learning, particularly relates to the technical field of medical image processing, and is used for solving the problem of low geometric fidelity of a segmentation result caused by hardening artifacts when an existing deep learning segmentation method is used for processing a high-density urinary calculus CT image. The method comprises the following steps: acquiring a urinary calculus CT image, performing initial segmentation by using a deep learning model to generate an initial calculus segmentation region, evaluating texture heterogeneity degree and identifying a hardening artifact risk region by analyzing feature value distribution of a structure tensor field, and positioning an artifact-causing source point based on a CT imaging projection geometric principle by reversely tracing a spatial position relation. According to the method, boundary distortion features are identified by analyzing CT value profile curve form distortion features and local boundary curvature singularity features, geometric correction is performed on corresponding boundaries in an initial stone segmentation region according to the boundary distortion features, a final stone segmentation region is obtained, and the geometric accuracy and reliability of a segmentation result are effectively improved.

The invention discloses a microcirculation blood vessel automatic segmentation and quantification method based on enhanced feature fusion. The method comprises the following steps: step 1, data preparation and preprocessing; 2, designing an enhanced feature fusion network model; step 3, automatically segmenting microcirculation blood vessels; and 4, quantifying the diameter of the blood vessel based on the segmentation result. According to the enhanced feature fusion network model provided by the invention, accurate segmentation of microvessels is realized through fusion and enhancement of multi-scale features. The model shows good generalization ability on diversified clinical image data through effective data enhancement, and can adapt to image data with different illumination conditions, contrast ratios and resolutions. Therefore, the model has high practicability and reliability in practical application. According to the method, the diameter of the blood vessel is quantified through the segmentation result, the diameter of the microvessel can be accurately measured, and powerful support is provided for evaluation of the clinical microcirculation function.

The invention relates to a medical image automatic segmentation method based on semi-supervised learning, and belongs to the technical field of medical image accurate segmentation. The method comprises the following steps: S1, proposing an automatic segmentation task of a medical image, wherein the method comprises the following small steps: S11, establishing a DulecrASE-Net semi-supervised network structure; s12, an MSA-RFSEU-Net network structure is established; s13, designing an adversarial two-stage consistency training strategy; s14, designing a multi-scale attention component DyMAC based on dynamic convolution; s15, designing a combined loss function; s2, establishing a medical image data set to carry out model evaluation, wherein the model evaluation comprises the following steps: S21, selection of evaluation indexes; s22, verifying the effectiveness of the evaluation model; and S23, verifying the universality of the evaluation model. According to the method, the medical image focus area can be effectively segmented, the segmentation precision can be improved to a certain extent through the designed antagonistic two-stage consistency training strategy, and the method can be used for the precise segmentation occasion of the medical image.

The invention discloses an abdominal multi-organ CT image automatic segmentation method based on deep learning. The method comprises the following steps: establishing a training sample set; constructing an improved encoder; an improved decoder is constructed; a PCE-TransUNet segmentation network model is established, and the PCE-TransUNet segmentation network model is Training the PCE-TransUNet segmentation network model by using the training set, and optimizing by using a joint loss function of cross entropy loss and Dice loss to obtain a trained PCE-TransUNet model; and inputting the test set into the trained PCE-TransUNet model, and outputting a segmented image by the PCE-TransUNet model. According to the method, partial convolution and an efficient channel attention mechanism are introduced, the ability of the model to extract image details is enhanced, the problem that feature extraction is insufficient in a traditional method is solved, and especially when small organs and complex boundaries are processed, the segmentation precision is remarkably improved.

The invention relates to an abdominal CT multi-organ segmentation method based on a LoRA vertical domain MedSAM large model. The method comprises the following steps: step 1, preprocessing an abdominal CT image supervised learning data set; step 2, constructing a feature selection network; step 3, constructing a LoRA layer; step 4, performing interlayer context reasoning by using a mixed prompt strategy to realize abdomen CT automatic segmentation; the method has the advantages that through multi-feature information fusion, dynamic context prompting and self-adaptive LoRA fine tuning, the precision and robustness of the MedSAM large model in the abdomen multi-organ CT segmentation task are improved, multi-feature information can comprehensively represent organ features, context information can be effectively utilized through dynamic context prompting, and the accuracy and robustness of the multi-organ CT segmentation task are improved. Limited training data can be effectively utilized by self-adaptive LoRA fine tuning; according to an image encoder based on a MedSAM large model, a learnable LoRA layer is added to each Transform layer so as to achieve efficient parameter training, and for a mask decoder of the MedSAM large model, the LoRA layers are also added to a self-attention layer and a cross-attention layer at the same time.

The invention belongs to the technical field of image segmentation, and discloses a multi-modal medical image automatic segmentation method and system based on a three-dimensional reconstruction model.The multi-modal medical image automatic segmentation method comprises the steps that multi-modal image data are acquired, and local features of the multi-modal image data are extracted; identifying a patient tissue structure in the multi-modal image data based on the local features; segmenting the tissue structure of the patient in the multi-modal image data by using a segmentation technology, and reconstructing a three-dimensional virtual model based on the tissue structure of the patient; and fusing a three-dimensional virtual model body drawing result and a three-dimensional virtual model surface drawing result to realize three-dimensional display of a segmentation result. According to the method, the influence of human errors and subjective judgment is reduced by identifying and segmenting the tissue structure, so that boundaries of various tissues are identified more accurately, automatic image segmentation can be completed in a short time, and subsequent diagnosis and analysis can be performed more quickly.

The invention discloses a swallowing function evaluation method and system based on tongue motion nuclear magnetic image registration, and belongs to the technical field of medical image processing. The system comprises a time sequence nuclear magnetic image reading module, a tongue body dynamic mask generation module, a tongue body dynamic image extraction module, a dynamic tongue body sequence registration module and a patient swallowing recovery evaluation module. Through an automatic segmentation and image registration technology, the system can accurately extract tongue motion displacement and calculate a local displacement vector, and evaluate the swallowing function recovery condition of a tongue cancer postoperative patient. The method comprises the steps of image reading, image screening, data set making, model training and tongue swallowing recovery evaluation, and the system can identify a tongue motion abnormal mode and provide an objective and quantitative evaluation result. According to the method, the accuracy and efficiency of tongue motion feature analysis are remarkably improved, and the method has a wide clinical application prospect.

The invention discloses a tumor image segmentation method and system of a multi-scale feature fusion network based on boundary enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented tumor image; constructing a tumor image segmentation model based on a pyramid visual converter PVTv2 backbone network; training the tumor image segmentation model through the to-be-segmented tumor image and the known tumor image to obtain an optimal tumor image segmentation model; and acquiring a real-time to-be-segmented tumor image and inputting the real-time to-be-segmented tumor image into the optimal tumor image segmentation model to obtain a tumor image segmentation result. Aiming at the endoscope image segmentation of the kidney tumor, the kidney tumor in the endoscope image is efficiently, stably and automatically segmented through the boundary-enhanced multi-scale feature fusion network, clinical doctors are helped to provide accurate tumor area positioning in endoscopy and surgical operations, and compared with the most advanced method, the method has the advantages that the accuracy is high, and the efficiency is high. According to the method, better segmentation capability and stronger generalization capability are obtained.

The invention discloses a mandibular neural tube automatic segmentation method based on multi-view guidance, and relates to the technical field of oral medical treatment. Comprising the following steps: collecting original oral cavity CBCT image data, and performing preprocessing; sending the preprocessed image data into a mandible segmentation network, and segmenting a mandible region containing a mandible neural tube; a contrast enhancement technology is adopted for the segmented mandible area; projecting the processed mandible image along three orthogonal tangent planes of a coronal plane, a sagittal plane and a cross section; performing key point detection on the mandibular neural tubes on the three projection planes by using the trained key point detection network to obtain shape information of the mandibular neural tubes; and the features are combined with texture direction features to be input into a three-dimensional CBCT mandibular neural tube segmentation network, and accurate 3D segmentation is carried out. Two-dimensional missing space information is avoided, and mandibular neural tube segmentation is more complete and accurate.

According to various examples of the present disclosure, there is provided a machine learning image segmentation model for automatically identifying and segmenting structural features of a vessel tree from image frames. The model comprises a 3D encoder and 2D decoder, the 3D encoder and 2D decoder connected by at least one interlinked convolution node and a plurality of temporal extraction nodes therebetween. The model is configured to identify and segment structural features of a vessel tree from the plurality of image frames, by the model being configured to: receive a plurality of image frames as an input to the 3D encoder; provide an output of the 3D encoder as an input to the plurality of temporal extraction nodes to extract temporal information; generate, by the plurality of temporal extraction nodes, a 2D temporal output based on the extracted temporal information; provide the generated 2D temporal output to the at least one interlinked 2D convolution node and 2D decoder; generate, at the 2D decoder, a combined temporal output based on an output of the at least one interlinked 2D convolution node and at least one temporal extraction node, wherein the combined temporal output represents a predicted segmentation of the vessel; and generate an output representative of the segmented structural features of the vessel tree based on the predicted segmentation.

The invention discloses a medical image automatic segmentation method of a U-shaped network based on channel mixing and a self-attention mechanism. According to the method, channel grouping and a disturbance strategy are introduced into a self-attention mechanism, so that the problems of channel redundancy, high operation cost and the like of a traditional self-attention method are effectively relieved; and the capability of extracting local space information and global channel information by the convolutional layer and the capability of extracting long-distance space dependence and modeling local channel information by the self-attention layer are combined more closely, so that the method has more comprehensive modeling capability for features. According to a network used in the method, firstly, an input medical nuclear magnetic resonance image or a computed tomography image passes through a down-sampling layer to preliminarily extract feature information and reduce the spatial dimension of the image, then a feature map is sent into a multi-level symmetric encoder-decoder network, and each level comprises a designed module; the module structure comprises a patch embedding module formed by depth separable convolution, a self-attention layer based on channel grouping and channel disturbance, and a bottleneck convolution layer formed by convolution layers of different dimensions. Wherein the patch embedding module is used for extracting position information, and the middle self-attention layer is used for extracting a long-distance dependency relationship and extracting local channel information through channel grouping and disturbance. The bottleneck convolution layer is used for exploring inductive bias and aggregating global channel information. Besides, in the jump connection process of the decoder, an efficient fusion module of channel and space cross attention is constructed, and the recovery capability of the feature information is further enhanced. The provided model is evaluated on a plurality of common data sets, and experimental results show that the method makes full use of channel information and spatial information between features, obtains a better segmentation result compared with an existing method, can be used for improving the performance of medical image segmentation, and has important application prospects and practical significance.

The invention relates to a small tumor automatic segmentation method for a kidney magnetic resonance image. The method comprises the following steps: S1, preprocessing an image for training and a tumor segmentation image; s2, constructing a 3D U-Net segmentation network model with multi-scale supervision; s3, carrying out pre-training on the 3D U-Net segmentation network model by using a Model Genesis self-supervised learning pre-training method so as to obtain model parameters; s4, training CP, DP, T1WI, NP and T2W1 sequences on the preprocessed image for training and the tumor segmentation map by using model parameters obtained by pre-training and adopting five-fold cross validation so as to enable each sequence to obtain five segmentation network models; s5, preprocessing the image to be segmented; and S6, based on the five segmentation network models, reasoning the preprocessed to-be-segmented image by using a sliding window method, and averaging the obtained initial segmentation results to obtain a final segmentation result. According to the invention, accurate and automatic segmentation of the small tumor of the kidney magnetic resonance image can be realized.

The invention belongs to the technical field of image recognition, and discloses a pelvic medical image automatic segmentation model based on global-local feature optimization. According to the model, an efficient non-local attention mechanism and a double attention mechanism are combined, global anatomical structure understanding and local fracture feature extraction are collaboratively optimized, and a three-level optimization strategy is adopted to achieve collaboration of anatomical constraint and pathological response. Specifically, the efficient non-local attention mechanism can enhance the global perception ability of the model and help the model to better understand a complex pelvic anatomical structure; and the double attention mechanism is helpful for the model to pay attention to details of a local area, such as changes of a fracture edge and a lesion area, so that the segmentation precision is improved. Meanwhile, according to the scheme, a mixed loss function combining label distribution perception loss and a surface supervision strategy is provided, the segmentation precision of an edge region is optimized through a guide network, the probability of missing detection and misjudgment is reduced, and then the robustness of the model in a complex pathological state is enhanced.

The invention belongs to the technical field of quality detection, and discloses an industrial visual identification method and system based on deep learning. The method comprises the following steps: constructing a high-quality welding image sequence through multi-angle image acquisition, multi-scale noise reduction optimization and welding spot feature enhancement processing; adopting adaptive feature extraction and cross-scale feature fusion technologies to generate a fusion welding feature tensor; the attention mechanism enhancement and automatic segmentation technology is utilized to realize accurate extraction of a welding spot area and microdefect enhancement identification; combining historical welding quality evaluation data to perform dynamic characteristic correlation analysis, and constructing a quality evaluation model; establishing a welding quality diagnosis strategy through multi-dimensional defect type identification and self-adaptive quality grade evaluation; and finally, dynamic quality prediction and real-time monitoring of the welding spot defects are realized. Accurate evaluation and real-time monitoring of the welding quality are achieved, and the quality cost and the rework rate are effectively reduced.

The invention provides a cervical cancer MRI image automatic segmentation method based on multi-modal fusion, and the method comprises the following steps: S1, obtaining tumor segmentation mask images of three modals T1c, T2 and DWI of a cervical cancer MRI image through an automatic prompt segmentation method, and carrying out the cross-modal attention interaction of a task token and an output token, dynamic alignment of medical image features and segmentation tasks is achieved, and segmentation mask images of all modes are generated in combination with sparse and dense prompts; and S2, performing registration and feature extraction on the segmentation mask images of the three modes to obtain a structured medical description of the segmentation mask image of each mode, and performing fusion processing on the structured medical descriptions by using a large language model to obtain a tumor segmentation mask image after multi-mode fusion. According to the method, the segmentation mask image of the MRI multi-mode image is fused by introducing the large language model, and the fused tumor segmentation mask image is finally obtained by combining the segmentation characteristics of different modes, so that the segmentation precision of the MRI image is greatly improved.

Approaches are disclosed herein for an automatic segmentation labeling system that identifies objects for potential open-class categories and generates segmentation masks for objects. The disclosed system may use a training pipeline that trains two segmentation models. The training pipeline may take, as input, a set of images with bounding boxes and class labels. The set of images may be fed into a first segmentation network with the bounding boxes used as ground truth for weak supervision. The first segmentation network may be trained to generate pseudo segmentation masks. In a second stage, the trained first segmentation network is used to generate pseudo masks for a set of input images. The generated pseudo masks are provided as input, along with the corresponding images, to a second segmentation network to be used as a type of ground truth data for training the second segmentation network to generate high-quality segmentation masks.

The invention provides a medical image automatic segmentation method and device based on a variational hybrid expert model, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring a three-dimensional medical image, and inputting the three-dimensional medical image into a U-Net encoder for multi-level convolution, down-sampling feature extraction and adaptive dictionary enhancement operation to generate each level of enhanced feature map; inputting the enhanced feature map of the last level into a variational hybrid expert module to perform shared feature extraction, and outputting and fusing with dynamic routing selection results of a plurality of expert models to generate fused features; inputting the fused features and the enhanced feature map of the last level into a U-Net decoder for up-sampling operation, and splicing the output of up-sampling and the enhanced feature map of the current level step by step to recover the spatial resolution of the image to obtain decoded fused features; and inputting the decoding fusion feature into a single-channel convolutional layer, and outputting a segmentation result. According to the method, the accuracy and robustness of medical image segmentation and the adaptive capacity to the multi-modal medical image are effectively improved.