647 results about "Automatic segmentation" patented technology

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 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 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 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 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 embodiment of the invention provides a lightweight segmentation method and system for a medical image. The method comprises the following steps: acquiring a to-be-identified multi-modal medical image; performing down-sampling on the to-be-identified multi-modal medical image to obtain a shallow feature map and a deep feature map; performing multi-scale feature extraction and fusion on the deep feature map to obtain multi-scale features; performing up-sampling on the multi-scale features, and fusing the multi-scale features with the shallow feature map to obtain features to be segmented; and performing segmentation according to the segmentation features to obtain a segmentation result. According to the method, the deep feature map and the shallow feature map can be obtained through down-sampling, then the multi-scale features are obtained through extraction and fusion of the multi-scale features, then the to-be-segmented features are obtained through up-sampling and feature fusion, and therefore the size and / or position information of a target focus is obtained through feature segmentation. Therefore, automatic segmentation can be realized through the model, and the size and / or position information of the target focus can be obtained.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

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.

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 discloses a multi-scale lightweight brain tumor segmentation method based on improved YOLOv8n-Seg, belongs to the technical field of medical image processor artificial intelligence, and designs a novel down-sampling module FLRDown fusing low-rank convolution and Fourier transform to replace standard convolution operation in a YOLOv8n-Seg backbone network. Further improvement is carried out on the basis of a SimAM attention mechanism, and a novel attention module-AdaSimAM is provided; on the basis of an original YOLOv8n-Seg segmentation header, an LCSDSH segmentation header network is designed, the structure of the header network is optimized, and redundant parameters are reduced. According to the method, a structure optimization and feature enhancement mechanism is introduced, the calculation complexity of the model is effectively reduced, meanwhile, the perception ability for a small target area is enhanced, the robustness and generalization ability of the model in multi-scale tumor recognition are improved, and therefore the method is more suitable for the actual requirement for automatic segmentation of the brain tumor in clinical practice.

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 fatigue crack automatic segmentation and quantification method and device in a structural strength test, and the method comprises the steps: obtaining a material fatigue crack image, and constructing a material fatigue crack image data set; a fatigue crack segmentation and quantification network CrackDAM-Net is constructed; comprising the following steps: constructing a local feature encoder, a global information encoder, a multi-scale feature fusion module, a decoder module and a crack length automatic quantization module, and training a fatigue crack automatic segmentation and quantization network; and inputting a crack image to be detected into the fatigue crack segmentation and quantization network CrackDAM-Net, and carrying out crack segmentation and length quantization to obtain the fatigue crack length. According to the method, the calculation complexity of the model is reduced by constructing the fatigue crack segmentation and quantification network-CrackDAM-Net; the effective fusion of fine-grained details and coarse-grained information is realized, and the extraction effect of crack global information, the accuracy of crack segmentation and the precision of crack length measurement are improved.

The invention relates to the technical field of computers, and discloses an automatic segmentation and intelligent plan generation method and device suitable for self-adaptive radiotherapy of prostate cancer. Comprising the following steps: acquiring medical image data of a treated object, clinical dose data of a target area and an organ at risk in a focus area of the treated object, and clinical treatment data of a preset priority level; performing automatic segmentation based on the medical image data to obtain target segmentation data; performing dose distribution prediction according to the target segmentation data to obtain initial dose distribution data, and performing iterative optimization on the initial dose distribution data by using clinical treatment data to obtain target dose distribution data of the treated object; and performing iterative optimization of a radiotherapy plan based on the target dose distribution data, and generating a target radiotherapy plan of the treated object. Therefore, on the basis of realizing accurate, efficient and automatic segmentation of the prostate cancer target region and the endangered organ, multi-round iterative optimization of dose distribution and the radiotherapy plan is carried out, and finally a high-quality target radiotherapy plan is obtained.

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 few-sample medical image segmentation method based on a human-in-loop and visual basis model. The method is characterized by comprising the following steps: firstly, carrying out diversified data enhancement on a single annotated image to construct a rich support set; then automatically selecting an optimal support image as a mask prompt for each image to be segmented through a dynamic matching algorithm, and driving the visual basic model SAM2 to realize automatic preliminary segmentation; and then, correcting an initial segmentation result by utilizing expert interaction feedback, generating a mask prompt enhancement signal, and iteratively optimizing the segmentation result of the whole sequence through a mask prompt mechanism of a visual basic model. Compared with the prior art, the method has the advantages that expert knowledge is fully utilized, efficient cooperation of automatic segmentation and expert correction is achieved, the medical image segmentation precision is remarkably improved, the problems of blurred target areas, unclear boundaries and the like in the medical images are effectively solved, the automation degree of the medical image analysis process is greatly improved, and the medical image segmentation efficiency is improved. Good clinical application prospects are realized.

The invention relates to the technical field of crop monitoring, in particular to an automatic cell segmentation method for a crop remote sensing image based on geographic prior information. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; performing semi-automatic segmentation on an unplanted bare soil region and a region of crops with unknown growth vigor; aiming at a crop area with known growth vigor, carrying out batch full-automatic segmentation processing on the multi-source sensor image by adopting an improved Ground-SAM segmentation large model, and outputting a segmentation result; and carrying out optimization processing on the segmentation result, and finally outputting an optimized cell segmentation map of the multi-source crop remote sensing image. The method has the advantages of accurate registration, efficient segmentation, multi-sensor compatibility and automatic processing, and remote sensing monitoring is improved.

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 present invention provides a method for automatic segmentation of fundus blood vessels based on low-cost noise data, including: S1: screening the data set, obtaining retinal images and pixel-level blood vessel labels through publicly available data sets; S2: constructing a convolutional neural network to generate quantifiable noise data; S3: segmenting the data set to construct a training set and a test set, and at the same time retaining one clean image data in the training set and applying corresponding noise to other images; S4: setting a loss function and training parameters; S5: alternately inputting the clean image data and the noise data into the network, and updating the network parameters by using a reweighting function; S6: obtaining a retinal image and inputting it into the trained network model to output a segmentation result. The present invention realizes that only a small amount of clean data can correct the bias brought by incorrect noise to the deep model, and improves the accuracy of automatic segmentation of fundus blood vessels when facing low-cost noise data.

The invention discloses a semi-supervised medical image automatic segmentation method based on pseudo label optimization. The method comprises the following steps: step 1, preprocessing and dividing a data set; 2, constructing a semi-supervised training framework which comprises a pseudo label refining module, a triple loss module and a mutual correction framework; refining processing is carried out through an SLIC superpixel segmentation method and an information entropy voting mechanism; a triple loss function is controlled to be minimized; a mutual correction loss function is controlled to be minimized; step 3, constructing a supervision loss function based on the result of the supervision test; the total loss function convergence is controlled; and step 4, segmenting the verification set, and evaluating segmentation results based on two indexes of a Dice coefficient and an ASD average surface distance. According to the method, a semi-supervised training framework is constructed based on a pseudo-label refining module, a triple loss module and a mutual correction framework, the number of pseudo-labels is increased, the spatial consistency and accuracy of the pseudo-labels are improved, and the capture capability and segmentation precision of boundary information are optimized.

The invention provides a multi-task brain glioma automatic segmentation and IDH genetic typing method based on SAM. Cooperative optimization of segmentation and typing tasks is achieved. The method comprises the following steps: acquiring a brain glioma multi-mode MRI image data set; preprocessing the data set to obtain a preprocessed standardized data set, and dividing the standardized data set into a training set and a test set according to a preset proportion; establishing a multi-task brain glioma automatic segmentation and IDH genotyping model based on SAM, wherein the model comprises a four-branch image encoder, a feature fusion module, a prompt encoder, a mask decoder and an IDH classifier; inputting the training set into the model for training, setting a joint loss function, and optimizing parameters of the model through the joint loss function to obtain an optimized model; and inputting the test set into the optimized model for segmentation and IDH typing and prediction to obtain the tumor segmentation precision and IDH typing accuracy of the optimized model.

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.