448 results about "Automatic segmentation" patented technology

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 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 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 the cross technical field of computer vision and civil engineering, and discloses a deep learning-based bridge disease image segmentation method, which comprises the following steps of: establishing an image data set containing crack and spalling diseases and performing online enhancement; constructing a segmentation network model comprising a frequency dynamic convolution encoder branch, an edge enhancement Transform encoder branch, a gating cooperation unit, a decoder and a depth supervision module; training the model by using a weighted mixed loss function; and inputting the test set to obtain a final segmentation mask. Self-adaptive fusion of local texture features and global context information is realized through a dual-encoder architecture and a gating cooperation mechanism; a frequency dynamic convolution and edge enhancement module is utilized to enhance the anti-noise capability and micro-disease perception under a complex background; and in combination with a category weighting strategy, the problem of pixel category imbalance is effectively solved, and high-precision automatic segmentation of concrete bridge diseases is realized.

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.

The invention specifically discloses an automatic generation method for a cervical vertebra disease rehabilitation prescription, and the method comprises the steps: constructing a cervical vertebra image feature automatic measurement system based on computer vision and deep learning, so as to monitor the training motion quality of a patient in real time, and obtaining the kinematics parameters of rehabilitation exercise training; acquiring a medical image and clinical data of a patient, and standardizing the medical image to construct a standard space of a multi-modal medical image; performing automatic segmentation and anatomical structure calibration on a vertebral body-intervertebral disc based on the standard space to obtain fusion image features; and constructing a multi-modal hierarchical decision model fusing the image features, the kinematics parameters and the clinical data to carry out quantitative analysis on pathological feature parameters, and outputting a standardized illness state assessment result conforming to international clinical guidelines. The dynamic optimization of the treatment scheme can be realized through a feedback mechanism, and the treatment effect is improved.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a handwritten element automatic segmentation and extraction method for a complex layout, relates to the technical field of handwritten element automatic segmentation and extraction, and aims to solve the technical problem that the recognition and separation precision of handwritten contents in a mixed image-text layout is insufficient. S201, a dynamic threshold segmentation algorithm is carried out; s202, context sensing connected domain analysis is carried out; s203, judging whether the elements are handwritten elements or not; s204, if the judgment result is yes, the handwriting region candidate is reserved; and S205, if not, filtering and eliminating. According to the method, the dynamic threshold segmentation algorithm and the context sensing connected domain analysis technology are cooperated, the segmentation threshold can be adaptively adjusted according to the pixel mean value and the standard deviation of the image local window through dynamic threshold segmentation, and the context sensing connected domain analysis is combined with the context information of the document to perform semantic analysis on the connected domain. The problem that the recognition and separation precision of the handwritten content in the mixed image-text layout is insufficient is solved.

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

The invention discloses a railway track detection method based on laser point cloud, which comprises the following steps: acquiring and preprocessing railway scene laser point cloud data, and constructing an enhanced training set and a verification set; constructing a dual-branch coding and decoding network model based on Transform, and training the network model based on the training set and the verification set; segmenting a large-scene point cloud in a random sampling mode in a reasoning stage according to the trained network model, accelerating neighborhood retrieval through spatial index, performing network reasoning on each segmented point cloud block, and fusing an overlapping region prediction result by adopting a probability weighting strategy to obtain a track category point cloud; carrying out a clustering algorithm on the track category point cloud to separate independent track instances and reject abnormal points in the vertical direction, and fitting a track center line for the remaining points by using a spline curve; and performing track linear optimization according to the track center line to obtain a track detection result. According to the method, high-precision and full-automatic segmentation and extraction of the track in a complex railway scene can be realized.

The invention discloses a PET / CT head and neck tumor automatic segmentation method based on a fusion diffusion model, and the method comprises the steps: carrying out the preprocessing of input data, carrying out the extraction of exclusive features of a PET / CT image through CFE, carrying out the correction of the features through TAS through region / edge loss, carrying out the construction of a condition tensor through the corrected features and an original image, inputting DDPM, and carrying out the denoising of a condition, and generating a final segmentation result; according to the customized feature extraction, an exclusive extraction strategy is designed for PET metabolism and CT anatomical characteristics, the modal adaptation defect of a single encoder is made up, the pertinence and expressive power of cross-modal features are remarkably enhanced, task-oriented auxiliary supervision improves the segmentation precision through double constraints of region and edge loss, robustness is enhanced, and the segmentation efficiency is improved. A condition tensor is formed by cascading a PET / CT original image and customized features in an early channel to serve as diffusion trunk input, so that fine-grained clues are continuously transmitted in a fidelity manner in a diffusion link, small target information dilution and missing detection caused by late fusion are reduced, and the recall rate of small-size and weak-boundary lesions is increased.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

The invention discloses a double-background static throwing object detection method based on automatic segmentation of a target area, and the method comprises the steps: obtaining a frame sequence of a to-be-processed video, generating a mask with a target area as a guide, and carrying out the constraint of the frame sequence of the video; extracting the ROI region of each frame in the constrained frame sequence of the video to obtain an image with enhanced edge continuity; obtaining an enhanced video frame sequence corresponding to the constrained video frame sequence, constructing a long-term buffer area, generating a long-term background image, and performing foreground difference processing to obtain a foreground mask; constructing a short-term buffer area, loading the foreground masks into the short-term buffer area, when the short-term buffer area reaches a preset capacity, executing pixel-by-pixel logic and operation on all the foreground masks, generating static masks, performing morphological closed operation processing, obtaining enhanced static masks, performing contour detection, and obtaining a boundary contour of a target area; and screening and marking a target area. According to the invention, accurate identification of the target area can be realized.

The invention relates to the technical field of computer vision and soybean plant phenotype measurement, and discloses a soybean plant phenotype analysis method, system and device based on three-dimensional reconstruction, and the method comprises the following steps: setting a device for carrying out multi-view image collection on a soybean plant to collect multi-view soybean plant images; and an end-to-end soybean plant leaf phenotype measurement model taking Pointnet Transform as a trunk is constructed, and the model can directly predict key phenotype characteristics such as leaf area and leaf perimeter from high-precision leaf point cloud data. Discrete point cloud voxelization processing needs to be carried out before the model is constructed, and conditions are provided for semantic segmentation. According to the method, automatic acquisition of the soybean plant point cloud, automatic segmentation of the soybean plant point cloud and automatic measurement of the soybean plant leaf point cloud phenotype are realized, a data basis is provided for soybean plant phenotype analysis, and high-throughput and intelligent soybean plant phenotype analysis is realized.

The invention relates to the technical field of digital medical treatment, and discloses a dental three-dimensional identification and model generation method based on CBCT and deep learning, and the method comprises the steps: carrying out the preprocessing of obtained CBCT image data; the preprocessed CBCT image data is segmented, and the segmentation step comprises the substeps that a tooth three-dimensional model, an alveolar bone three-dimensional model and a mandibular neural tube three-dimensional model are obtained by adopting a two-stage cascade deep learning network; based on the tooth three-dimensional model and the alveolar bone three-dimensional model, obtaining a periodontal membrane three-dimensional model through geometric and Boolean operation; a tooth root area in the tooth three-dimensional model is positioned, and a self-adaptive threshold method is adopted for CBCT image data corresponding to the tooth root area, and a root canal three-dimensional model is recognized and generated; based on at least one of the obtained three-dimensional models, a personalized clinical application model is generated, high-precision automatic segmentation of the dental complex anatomical structure is achieved, and a reliable digital model basis is provided for personalized precision medical treatment.

Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and / or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

The invention provides a brain tumor segmentation method based on Dual-SwinTransBTS, and mainly relates to the technical field of medical image segmentation. Comprising the following steps of: 1, constructing a multi-modal cross attention module (MCA) based on a Swin-Transform and a Swin-Transform interactive fusion module (STFusion), and constructing the multi-modal cross attention module (MCA) based on the Swin-Transform and the STFusion module (STFusion) based on the Swin-Transform; 2, constructing a brain tumor segmentation model Dual-SwinTransBTS in combination with the MCA module and the STFusion module; 3, data preprocessing, data division and data enhancement; fourthly, the training set obtained after preprocessing is input into a Dual-SwinTransBTS segmentation model to be trained; 5, inputting the multi-mode nuclear magnetic resonance imaging data to be segmented into the Dual-SwinTransBTS brain tumor segmentation model, and carrying out segmentation on the multi-mode nuclear magnetic resonance imaging data to be segmented; according to the invention, the problem of poor automatic segmentation effect of the existing multi-modal nuclear magnetic resonance imaging data can be solved.

The invention discloses an optic cup and optic disc segmentation method and system based on improved U-Net, and particularly relates to the technical field of medical image processing. The method includes: performing standardized preprocessing on an eye fundus image; the U-Net model is optimized and improved, and an improved model LFM-Net is obtained; the improved model LFM-Net comprises a multi-scale feature enhancement aggregation module and a trans-attention feature fusion module; the multi-scale feature enhancement aggregation module is used for capturing feature information of different scales, and the trans-attention feature fusion module is used for realizing fusion of features of an encoder and a decoder; and inputting the preprocessed eye fundus image data set into the improved model LFM-Net for training, and realizing automatic segmentation of an optic cup and an optic disk in the eye fundus image by using the trained model. According to the invention, by enhancing multi-scale feature expression and optimizing feature fusion efficiency, the problem of insufficient segmentation precision under complex conditions such as blurred optic cup and optic disc boundaries and irregular shapes is effectively solved.

The invention discloses a structured information extraction method and system based on a large language model, and the method comprises the following steps: carrying out the modular cue word design and adaptive loading, so as to guide the large language model to carry out the content generation according to the expected structure and logic; inputting text type identification and task routing to ensure that each type of information can be processed by adopting the most appropriate model prompt strategy; automatic segmentation and concurrent processing are carried out to ensure information integrity and processing efficiency; semantic completion and key information enhancement are carried out to carry out semantic completion on the entity information; performing structured format conversion and standard packaging to output a standardized format; and performing exception processing and full-process log recording to perform real-time tracking and backtracking on the state of each processing task. According to the method, the key information in the text can be automatically recognized, extracted and output in a structured mode, and the information processing efficiency and accuracy are improved.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

A plain-scan CT image aortic valve calcification segmentation system based on space-time priori comprises a position priori information extraction module, an encoding module and a decoding module, after an aortic image is automatically segmented in an off-line stage to obtain a segmentation mask, attention weights are generated through preprocessing and encoding, and the aortic valve calcification is subjected to image segmentation; and inputting the aorta image serving as a training set into a segmentation model comprising a position prior information extraction module, a coding module and a decoding module, and performing real-time image segmentation through the trained segmentation model in an online stage. According to the method, calcification point features of different sizes and forms are captured through multi-scale prediction; the aorta segmentation prior is introduced, the sensitivity and positioning precision of the model to aortic valve calcification are improved, coronary artery opening sequence time sequence information is introduced, a double-branch structure is adopted to adapt to structural differences, accurate positioning and recognition of aortic valve calcification lesions are achieved, and the false detection rate and the omission ratio are effectively reduced.

The invention provides an industrial material segmentation and size measurement method based on deep learning. The method comprises the following steps: acquiring a to-be-processed industrial material image; inputting the image into the trained segmentation network model, and outputting to obtain a pixel-level segmentation mask; the segmentation network model is a hybrid network based on a U-Net architecture, a pre-trained ResNet50 is adopted by an encoder of the segmentation network model, and a convolutional block attention module (CBAM) is fused in jump connection between the encoder and a decoder; transforming the pixel-level segmentation mask to an aerial view space by using a perspective transformation matrix obtained by pre-calibration to obtain a corrected mask; in the aerial view space, geometric features of the corrected mask are calculated, and the physical size of the material is obtained through conversion according to a preset proportional scale. The device has the beneficial effects that high-precision and automatic segmentation and size measurement of industrial materials can be realized.

The invention discloses a skin lesion image segmentation method based on feature interaction fusion, and relates to the technical field of medical image segmentation. Comprising the following steps: firstly, carrying out size normalization and noise suppression on a dermatoscope image, and expanding a data set through random rotation and overturning operation; inputting the training data into the network model for training to obtain a trained model weight; and finally, inputting a to-be-segmented skin lesion image into the trained network model to realize automatic segmentation of a skin lesion area. According to the invention, a focusing feature interaction fusion module is designed, and deep complementary fusion of CNN and Transform features is realized; a multi-scale context attention module is constructed, multi-scale accurate feature extraction is realized by extracting spatial information from local scale to global scale, and meanwhile, the calculation efficiency is kept. The invention aims to solve the problems of complex shape, variable size, low contrast and the like of the lesion area of the skin lesion image, and improve the segmentation precision and robustness of the skin lesion image.

The invention relates to the field of three-dimensional vision, in particular to a label-free three-dimensional point cloud segmentation method based on a visual large model, which comprises the following steps: acquiring three-dimensional point cloud data, and extracting a boundary point set according to the three-dimensional point cloud data in combination with curvature and normal vector to construct a target function; solving the objective function by using an optimization algorithm, selecting an optimal projection visual angle combination, and extracting two-dimensional features of all two-dimensional projection images and three-dimensional features of three-dimensional point cloud data by using a visual large model; performing consistency matching on the two-dimensional features and the three-dimensional features through a matching algorithm to obtain a plurality of matching results; acquiring a definition weight factor and a coverage rate weight factor to calculate an importance weight; fusing the two-dimensional features according to the weight factors to obtain overall fused two-dimensional features; and inversely mapping the integrally fused two-dimensional features to a three-dimensional space to realize three-dimensional point cloud unmarked segmentation. The three-dimensional point cloud segmentation method has the effect of realizing three-dimensional point cloud automatic segmentation without labels.

The invention discloses a pulmonary interstitial image analysis method and system based on clinical prior guidance feature fusion, and the method comprises the steps: obtaining chest CT images of a user at a plurality of time points and corresponding clinical data, and carrying out the data preprocessing and region segmentation; performing feature extraction on the automatically segmented chest CT image and the corresponding clinical data by using specific indexes; specific time points are coded into time embedding vectors, total image feature vectors are projected to the same dimension as the time embedding vectors through a linear layer, and time coding fusion is generated; the most relevant CT image follow-up visit time points are actively'inquired 'and'weighted' by using clinical risk factors, time sequence image feature fusion is carried out, and fusion features after weighted fusion are output; and carrying out progress probability calculation by using the fusion features, predicting the progress risk in the next one year according to the calculation result, and realizing dynamic time sequence feature selection driven by clinical priori knowledge.

The invention relates to a method, a device, equipment and a storage medium for automatically segmenting a vehicle journey to a return stroke, and the method comprises the steps: obtaining the journey data of a vehicle in a current journey process after the current journey of the vehicle is completed; according to position changes and time intervals of adjacent records in the travel data, parking point identification is carried out, and a parking point set is obtained; determining a stroke segmentation point according to a linear distance between a parking point in the parking point set and a starting point in the stroke data; segmenting the stroke data according to the stroke segmentation points to obtain a segmentation result; the segmentation result comprises a forward travel track and a return travel track; by acquiring the travel data of the vehicle after the current travel is completed, recognizing the parking point by combining the position change and time interval dual features, and recognizing the travel segmentation point based on the linear distance to realize travel segmentation, the problem that the traditional method depends on manual judgment and the segmentation deviation is caused by neglecting the actual parking behavior in the prior art is solved.

This invention discloses a method, apparatus, medium, and equipment for detecting tracheid cavities in transverse sections of coniferous wood, belonging to the field of wood identification technology. Based on an active learning approach, this invention combines generative adversarial networks (GANs) for sparse data sampling and uses the output of a target detection model as a SAM (Self-Assisted Analysis) prompting strategy to achieve automatic segmentation of tracheid cavities in coniferous wood transverse sections and obtain quantitative anatomical data. By optimizing the data sampling process and enhancing the model's adaptability, this method effectively improves the accuracy and efficiency of cavity segmentation and can be adapted to any coniferous wood transverse section microscopic image. It achieves low-cost construction of a coniferous wood microscopic image database and the development of a model for the detection, segmentation, and measurement of coniferous wood tracheid cavities. This solves the problems of difficult manual detection and high collection and annotation costs caused by the complex structure and sampling difficulty of tracheid cavities in coniferous wood transverse sections, enabling automatic and rapid detection, segmentation, and measurement of coniferous wood tracheid cavities.

The invention discloses a quantitative characterization method for a multi-scale gamma'phase of a polycrystalline high-temperature alloy, which comprises the following steps: sequentially carrying out metallographic sample preparation, electrolytic polishing and gamma 'phase in-situ electrolytic etching on a standard high-temperature alloy sample, collecting a microscopic electronic image, and carrying out image processing, multi-scale gamma' phase labeling and data augmentation to obtain a training sample; iteratively training the U-Net convolutional neural network architecture by using the training sample to obtain a multi-scale gamma'phase feature extraction model; and inputting the gamma'phase secondary electron image of the surface of the polycrystalline high-temperature alloy to be detected into the multi-scale gamma 'phase feature extraction model to obtain a binary gamma' phase feature image, and performing statistical distribution characterization to obtain statistical distribution data of each gamma 'phase. According to the method, automatic segmentation identification and quantitative statistics of the multi-scale gamma'phase in the polycrystalline high-temperature alloy are realized by adopting in-situ electrolytic etching, high-flux scanning electron microscope acquisition, image super-resolution processing and a deep learning algorithm, and the speed, the accuracy and the engineering practicability of quantitative analysis of the gamma 'phase are remarkably improved.

The invention discloses a real-time medical image segmentation method and system, and the method comprises the steps: carrying out the pre-training processing of a basic model in a self-supervised learning mode, and enabling the basic model to learn domain invariant features from inter-frame dependence based on the time series data input of a medical image sequence; the multi-scale feature knowledge of the basic model is migrated to the lightweight detection model through feature distillation operation, and the feature distillation operation is configured to be based on a feature alignment mechanism, and fine features are not lost during knowledge transfer; and automatically generating segmentation prompt information by using a lightweight detection model, inputting the prompt information as spatial guidance into a segmentation model, driving the segmentation model to output a pixel-level result, and constructing an end-to-end flow workflow without manual intervention. According to the invention, through organic combination of self-supervised pre-training, feature distillation and automatic prompt generation, end-to-end automatic segmentation is realized.