89 results about "Tissue segmentation" patented technology

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

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 a pericardium adipose tissue segmentation method, device and equipment based on an MR image and a medium. The method comprises the following steps: acquiring a target heart magnetic resonance image containing pericardium adipose tissue of a target object; segmenting pericardium adipose tissue in the target heart magnetic resonance image based on a tissue segmentation model to obtain target pericardium adipose 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, and the three-branch cross-domain feature collaborative encoder performs feature extraction from global, local and frequency domains based on a three-branch structure; the double attention feature fusion module is established based on a space attention mechanism and a parallel channel attention mechanism, and the dynamic boundary perception decoder is used for enhancing the global and boundary perception capability. According to the scheme, the pericardium adipose tissue in the heart MR image can be accurately and efficiently segmented by using the tissue segmentation model based on multi-feature collaboration.

The invention relates to the technical field of medical training, and discloses a lumbar puncture simulation teaching system based on an AR technology, and the system comprises an AR content generation module which collects a lumbar medical image and carries out the tissue segmentation and focus recognition; performing three-dimensional geometric modeling based on the tissue segmentation result and the focus recognition result to generate a lumbar vertebra 3D model; the AR display interaction module develops an interaction function of the lumbar vertebra 3D model by using a Unity platform and AR equipment; interactive operation of the user is monitored, and feedback is conducted in real time; and the operation evaluation module is used for evaluating the interaction operation of the user and assigning an improvement suggestion according to an evaluation result. A three-dimensional visual scene is constructed through the AR technology, a user can observe the needle inserting process from different angles according to the complex anatomical structure of the lumbar vertebra part, and dynamic effects such as cerebrospinal fluid pressure change are simulated; in the lumbar puncture simulation training, the AR system can feed back operation data in real time, such as the needle inserting angle, the puncture strength and the puncture depth, and the user is helped to accurately master operation skills.

The invention discloses a benign and malignant identification and growth prediction system based on pulmonary nodule radiomics, and relates to the technical field of medical image processing. Comprising an acquisition module used for acquiring a pulmonary nodule segmentation mask, a CT value and surrounding tissue segmentation data; the calculation module is used for synchronously calculating a first score representing internal CT value distribution heterogeneity, a second score representing surface complexity and a third score representing blood vessel interaction according to the data; the judgment module is used for triggering a high-risk alarm when the scores of the three items all exceed corresponding threshold values; the prediction module is used for correcting the basic growth model according to the three scores and generating growth prediction data; and the output module outputs an alarm and a prediction result. The system also comprises optimization modules of weight fusion, time sequence processing, growth partitioning and the like. According to the scheme, through multi-dimensional feature fusion and dynamic modeling, more accurate identification of benign and malignant pulmonary nodules and more reliable growth trend prediction are realized, and key support is provided for clinical decision making.

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 invention discloses an image segmentation method, device and equipment for pericardium adipose tissue and a medium. The method comprises the following steps: acquiring a heart magnetic resonance image of a target object, wherein the heart magnetic resonance image comprises pericardium adipose tissue; segmenting the pericardium adipose tissue in the heart magnetic resonance image by using a pre-trained tissue segmentation model to obtain a target pericardium adipose tissue of the target object; the tissue segmentation model comprises an encoder, a decoder and a space channel attention module, the encoder comprises a double-branch cooperation module, the decoder comprises a multilayer fusion convolution module, the double-branch cooperation module is used for feature extraction based on a double-branch network, and the space channel attention module is used for feature enhancement in space and channel dimensions. And the multi-layer fusion convolution module is used for feature fusion based on grouped convolution. According to the scheme, semantic information and structural features in the image can be fully mined by using the tissue segmentation model, the segmentation performance in a complex scene is improved, and the pericardium adipose tissue is efficiently and accurately segmented.

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 present application relates to the technical field of medical image processing, in particular to a pancreatic lesion CT image positioning method and system based on multi-tissue segmentation, comprising the following steps: acquiring a CT image containing a pancreatic tumor; using a pre-established feature extraction network to perform feature extraction on the CT image to obtain image features; using a region proposal network to generate candidate regions for the image features to obtain pancreatic tumor segmentation candidate regions. The present application uses a global feature extraction network structure and a local feature extraction network structure as a teacher model to guide the training of a lightweight MobileNet network to construct a feature extraction network for pancreatic tumor positioning, which can achieve high-precision positioning performance by aggregating global and local features, that is, it can extract tumor lesions of multiple sizes and improve the lightweight structure to reduce the amount of calculation.

The invention relates to the technical field of medical image processing, in particular to a preoperative image segmentation method and system based on hybrid supervision enhancement, and the method comprises the following steps: constructing an organ tissue segmentation model by using a Unet network, and constructing a vascular tissue segmentation model by using the Unet network; setting a supervision enhancement module, and combining the organ tissue segmentation model and the vascular tissue segmentation model into a teacher model; a student model is constructed by using an FCN network, and a preoperative image segmentation model is obtained through mixed supervised learning of a teacher model and the student model. For preoperative three-dimensional medical image segmentation, a mixed supervision segmentation model is provided, a supervision enhancement mode is adopted when a teacher model is constructed, it can be guaranteed that the teacher model accurately segments each organization, and a lightweight model structure is obtained based on a teacher-student multi-fine-grained feature learning strategy; and a student model capable of precisely segmenting each organization can be realized, so that the segmentation precision and the segmentation efficiency coexist.

The invention relates to the technical field of medical image processing, in particular to a medical image adaptive segmentation method based on dynamic edge perception. The method comprises the following steps: preprocessing an input medical image; extracting a multi-scale feature map by using an encoder; calculating a feature difference degree through a dynamic edge sensing module to generate an edge weight map, and enhancing a feature edge response according to the edge weight map; integrating the edge features and the original features by adopting a self-adaptive fusion module; and finally, the resolution is recovered through up-sampling of a decoder, and a segmentation probability graph is output. According to the method, the edge features can be adaptively enhanced, multi-scale feature fusion can be optimized, and the edge accuracy and the overall precision of target tissue segmentation in the medical image are effectively improved.

The application discloses an oral cavity image multi-tissue full-automatic segmentation method based on artificial intelligence, relates to the technical field of oral cavity image multi-tissue full-automatic segmentation, and comprises the following steps: collecting and enriching annotated oral cavity image data; designing a data enhancement method to improve image quality; constructing a convolutional neural network model, introducing an attention mechanism to improve accuracy; designing a combined loss function, dynamically adjusting weights to balance multi-class segmentation; predicting and generating results for new samples, and performing post-processing optimization; evaluating different tissue segmentation effects to further improve the integrated model. The application greatly reduces the artificial annotation workload and improves the data set construction efficiency; realizes a dynamic weight adjustment mechanism of the loss function, can be suitable for multi-class medical image segmentation tasks, and improves the model generalization; and an automatic model integration and optimization method is proposed, which can continuously improve the model performance and output more accurate results.

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.

This invention discloses a method for training a brain tissue segmentation model using a single atlas image and its application, belonging to the field of medical image segmentation. The method includes: when training a segmentation network based on a registration-segmentation dual-model iterative learning, using the registration network to predict the atlas image I. a and unlabeled image I u The offset field φ between them, and the spectral image I according to φ. a and its label S a Perform deformation operations separately to obtain deformation atlas images and deformation atlas labels. Calculate the registration confidence map C, which is used to represent and I. u Alignment at each pixel; according to C-alignment and I-alignment. u Regions with varying degrees of alignment were individually subjected to IST style transfer and then stitched together to obtain a style-transferred image. Regions with higher alignment levels exhibited higher style transfer coefficients. This was used as supervisory information, with I... u Using the sum as input, the segmentation network is trained, with the training loss for the sum being a confidence-guided loss. This invention can improve the performance of brain tissue segmentation when only a single atlas image exists.

The application discloses a kind of based on hyperspectral image tissue segmentation method and system, comprising: by constructing feature distillation network and applying pathological priori constraint etc., fine segmentation is carried out to hyperspectral tissue image, accurate analysis to cell tissue is realized.Specific steps include dataset creation, training data expansion, feature distillation network construction, model training and optimization, test set forward calculation, pathological priori constraint calculation, tissue fine segmentation.The application has the advantages of high precision, self-adaptability, simple structure, strong practicability and optimizability, and can be widely applied in medical image diagnosis, biomedical research and other fields.

The invention provides a biological tissue image segmentation method based on deep learning, and aims to improve the segmentation accuracy of a tissue structure in a biomedical image with low contrast, more noise and fuzzy boundary, and the method comprises the following steps: constructing a U-shaped network model containing an improved convolutional neural network structure, the model has enhanced feature extraction capability and detail recovery capability; preprocessing the biological tissue image, enhancing the contrast ratio of the image and reducing noise; outputting a segmentation result of the biological tissue image through the U-shaped network; and finally generating a high-precision biological tissue segmentation image according to the cross entropy loss function and the Dess coefficient optimization model. The method is suitable for various biomedical image data including but not limited to computed tomography (CT), magnetic resonance imaging (MRI) and tissue slice images, can effectively improve segmentation precision and reduce calculation overhead, and has wide clinical application potential.

The invention discloses a whole tissue segmentation method based on multi-modal large model guidance, and the method comprises the steps: constructing a whole tissue segmentation label data set covering a path from skin to an internal endangered organ, carrying out the preliminary segmentation of a whole tissue structure based on a general segmentation model, and carrying out the segmentation of a whole tissue structure through an image self-adaption prompt box and a human-computer interaction mechanism. Guiding the model to complete iterative annotation and correction of an organization structure, and constructing standardized full-organization label data on the basis of the iterative annotation and correction; the multi-modal contrast learning model is finely adjusted, the to-be-segmented image is used as an image modal, the standardized organization template text is used as a language modal, and modeling training of a vision-semantic consistency relationship is carried out for subsequent significance guidance; a full-tissue segmentation model based on language image guidance and memory storage is constructed, a medical general segmentation model is used for fine tuning, a space adapter and a memory storage learner are designed for spatial relation learning, a multi-modal contrast learning model after fine tuning is used for generating a saliency heat map, and the saliency heat map is obtained. And full-tissue organ detection and fine classification segmentation based on text information guidance and spatial relationship learning are realized. Compared with an existing method, the method has the advantages that the problems that organ boundaries are broken, semantic tags are inconsistent, category matching depends on manual definition and the like are effectively relieved, the clinical availability is enhanced while the segmentation precision is improved, and high-quality whole tissue segmentation support is provided for tasks such as particle implantation path planning and the like.

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.