760 results about "Automatic segmentation" patented technology

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

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

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

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

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

The invention discloses a 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 present invention relates to the field of image processing technology, and discloses a method for automatic segmentation and recognition of ultrasound images based on deep learning, including the following steps: S1, data preparation and preprocessing, S2, self-supervised learning pre-training, S3, transfer learning and incremental learning, S4, multi-task joint learning, S5, image quality enhancement and super-resolution reconstruction: S6, multi-modal information fusion, information fusion by combining modal images, S7, model training and optimization, increasing data diversity by using data enhancement technology. By fusing ultrasound images with modal image information, the segmentation accuracy and lesion recognition ability of ultrasound images are improved, and different modal images provide complementary features, showing significant advantages in the recognition of complex lesions, complex locations, complex morphology and early lesions. At the same time, by fusing images of different modalities through a deep learning model, image quality can be improved and diagnostic accuracy can be improved.

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

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

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

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

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

The invention relates to the technical field of bone mineral density detection, and discloses a bone mineral density detection method which comprises the following steps: step 1) image acquisition; step 2) image segmentation; 3) reconstructing a three-dimensional surface; 4) reconstructing three-dimensional body distribution; 5) performing calibration calculation; according to the method, the automatic segmentation algorithm, the three-dimensional reconstruction algorithm and the gray density mapping technology based on the Lambert-Beer law are combined, so that the lumbar vertebra bone mineral density is rapidly and accurately detected, and a reliable basis is provided for early diagnosis of osteoporosis; in addition, the method is based on common X-ray images, is low in radiation dose, is lower in equipment cost, is suitable for basic medical institutions and resource-limited regions, and has wide popularization value and application prospect.

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

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

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

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

The embodiment of the invention provides a method and device for evaluating the health state of a front-end isolated switching power supply of an aging table, and relates to the technical field of aging tests.The method comprises the steps that in the aging test process, the front-end isolated switching power supply of the aging testbed can generate monitoring signals such as voltage and current; a directly measured monitoring signal is large in data size and contains a large amount of redundant information, firstly, feature extraction is conducted on directly measured data, and a feature sequence with high information density is obtained; further spatial mapping is carried out on the feature sequence by using a neighbor component analysis algorithm, and automatic segmentation of the health state and the degradation state of the new test sample is realized; and performing quantitative calculation of the health state on the mapped feature sequence by using a multi-distance measurement fusion algorithm with automatic weight optimization, and completing health assessment of the front-end isolated switching power supply of the aging test stand.

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

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.