755 results about "Accurate segmentation" patented technology

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

The invention relates to the field of image processing, in particular to a pin shaft forging forming quality detection method and system, and the method comprises the steps: carrying out the image collection of a to-be-detected pin shaft forge piece; then determining a total energy function of the active contour model and determining a neighborhood window, obtaining a structure tensor based on a gradient magnitude in the neighborhood window, and calculating to obtain local gradient direction dispersion; then, a defect edge structure enhancement index is calculated; then calculating a self-adaptive external energy scaling adjustment factor, and fusing the self-adaptive external energy scaling adjustment factor into an energy function of the active contour model to form an improved active contour model; and finally, accurately segmenting and extracting the surface defects of the pin shaft, and carrying out quality detection by combining the extracted defect characteristics. According to the method, by constructing local gradient direction dispersion and defect edge structure enhancement, a self-adaptive external energy scaling adjustment factor is calculated, and an active contour model is improved so as to accurately detect the surface defects of the pin shaft forge piece.

The invention relates to the technical field of image defect detection, in particular to an intelligent drug particle detection system and method based on image processing. According to the method, a high-resolution industrial camera and a polarized light source are used for dynamically shooting assembly line medicine particles so as to obtain multi-angle high-quality original images; noise suppression and microstructure enhancement are realized through multi-scale residual information enhancement and structure maintenance adaptive filtering, and the detail resolution is improved by using local contrast normalization; a multi-scale structure gradient of direction perception and region shape prior are fused, and a hierarchical contour evolution and dynamic threshold strategy is adopted to accurately segment a particle main contour and a microcrack; micro defects are identified based on a multi-scale texture sensing algorithm guided by a boundary relative potential graph, and multi-class defect classification is realized through a spatial and semantic dependency relationship between graph neural network modeling regions; and finally, a traceable quality detection report is generated. The intelligent level, the accuracy level and the automation level of medicine particle detection are improved.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention relates to the technical field of image processing, and provides an enteromorpha remote sensing image detection method and system based on deep learning, and the method comprises the following steps: carrying out the edge gradient extraction and small target enhancement processing of an obtained to-be-detected remote sensing image, and obtaining a first feature map after the edge enhancement and small target feature enhancement; transmitting the first feature map to a U-shaped backbone network formed by cascading multiple stages of Ep-VSS block modules, performing multi-stage feature extraction, and obtaining a detection result of the enteromorpha remote sensing image based on the feature map output by the last stage of Ep-VSS block module; according to the method, through edge enhancement, small target sensitive detection, multi-scale texture extraction and spatial context modeling, precise boundary segmentation and long-range dependence modeling are realized, and the accuracy, real-time performance and reliability of enteromorpha remote sensing monitoring are improved.

The embodiment of the invention provides a multi-modal large model image segmentation method and device based on hierarchical lexical representation, a mask image is coded into a lexical sequence by designing a mask marker, and progressive generation from a shape prototype to local details is realized through a causal attention mechanism. A three-stage training strategy is adopted, firstly, a marker is trained through a mask reconstruction task, then mask lexical elements are integrated into a large-scale multi-modal model and combined training is carried out, and finally fine tuning is carried out by utilizing high-resolution data. And performing multi-level supervision based on a hierarchical mask loss function to realize accurate segmentation of a natural language description target. According to the method, the defects of the traditional technology in the aspects of complex scene understanding, model training, mask generation and the like are effectively overcome, and the performance of multi-modal image segmentation is remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a segmentation and matching method, device, equipment and medium for an image background region. Multi-scale features are obtained through cavity convolution branch, fusion features are generated through weighted fusion, the fusion features are input into a decoder network to obtain a background segmentation mask, a background area image is obtained by combining the preprocessed image, and a visual converter model is input to extract background feature vectors; and performing similarity matching with a target feature vector in a preset feature database to retrieve a target image. According to the method, the segmentation accuracy and robustness are improved through combination of multi-scale feature fusion and the visual converter, meanwhile, the retrieval discrimination and reliability are enhanced through feature matching, the problems that an existing method is insufficient in segmentation precision and limited in retrieval capacity are solved, and precise segmentation and efficient retrieval of the background in a complex scene are achieved.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention relates to the field of medical image segmentation, and discloses a precision surgery-oriented robot medical image segmentation and feature extraction method, which comprises the steps of constructing a dynamic segmentation network model of a bidirectional attention architecture, and inputting a preprocessing module for feature extraction and generating a multi-scale feature pyramid; the Transform coding branch is used for time sequence feature modeling and outputting a time sequence enhancement feature; the convolutional coding branch is used for enhancing anatomical features and surgical instrument features and outputting spatial enhancement features; the multi-stage feature fusion unit is used for performing multi-stage iterative fusion and outputting final fusion features; the decoding output module is used for decoding and generating pixel-level segmentation masks of the anatomical structure and the surgical instrument; training the dynamic segmentation network model; and performing medical image segmentation and feature extraction based on a medical image video sequence input in real time by using the trained dynamic segmentation network model. Accurate segmentation of anatomical tissues and dynamic instruments in an operation scene is realized.

The invention discloses a virtual fitting video generation method and system based on multi-view face fixation, and relates to the field of generative artificial intelligence and computer vision, and the virtual fitting video generation method based on explicit geometric constraints comprises the following steps: S1, obtaining an original fitting image and an action cue word, and constructing a multi-source input data set; s2, segmenting the original fitting image to obtain multi-view modeling, and generating a multi-angle face image and a clothing texture feature parameter; s3, analyzing the action cue word to generate a target posture sequence, and generating head and tail frame virtual fitting images; s4, performing pairing analysis on the virtual fitting images of the head frame and the tail frame to generate attitude transition parameters; s5, performing dynamic texture repair on the image to generate a transition frame image sequence; and S6, generating a virtual fitting video and outputting the virtual fitting video. According to the method, accurate segmentation and multi-angle modeling of the face area and the clothing area are realized through image segmentation and a rigid geometric transformation algorithm.

The invention discloses a remote sensing image semantic segmentation method and system based on a dynamic attention mechanism, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: carrying out the labeling and enhancement processing of a multi-scene remote sensing image, and generating a standardized data set; processing the data set through an encoder, and extracting a multi-scale high-dimensional feature map; decoding the multi-scale high-dimensional feature map, and fusing a decoding result with scale features to generate an optimized feature map; based on the optimized feature map, adopting a joint loss function to synchronously optimize segmentation, detection and classification tasks; performing dynamic up-sampling and boundary refinement on the optimized feature map, and outputting a structured analysis result; according to the method, the boundary information of the ground objects in the remote sensing image is accurately extracted through a dynamic processing flow and introduction of a double-flow decoder network and a multi-task joint optimization strategy, accurate segmentation and classification of the ground objects in a complex scene are achieved, and the overall effect of analysis and processing of the remote sensing image is improved.

The invention relates to the field of image processing, and particularly discloses a man-machine collaborative remote sensing image labeling method based on an SAM model, which does not directly input sparse prompts of a user into the SAM, but firstly utilizes a pre-trained remote sensing semantic segmentation model to pre-compute an image to generate a semantic graph rich in surface feature category information. On the basis, single-point interaction of the user is combined with the semantic graph, dynamic semantic prompt enhancement is carried out, namely, a user intention area is automatically recognized, a dense enhancement prompt set with definite positive and negative attributes is generated, the enhancement prompt set can convert the fuzzy intention of the user into a guide signal capable of being accurately understood by a machine, and the user intention is automatically recognized. According to the method, the segmentation process of the SAM model is constrained, the segmentation result which is consistent in semantics and accurate in boundary is generated, and the optimal mask is further optimized through semantic consistency, so that the segmentation ambiguity is fundamentally solved, and the efficiency and accuracy of remote sensing image labeling are improved.

The invention relates to the technical field of deep learning and computer vision, and discloses a histogram interactive attention-based pavement crack segmentation network processing method and system, so as to enhance the edge detail fidelity and improve the crack segmentation precision. The method comprises the steps of image preprocessing, up-sampling, down-sampling, feature fusion and image reconstruction processing. Wherein global feature modeling in the intensity sub-boxes and among the sub-boxes is realized by constructing a histogram interactive attention module (HIA); a double-branch detail enhancement feedforward module (DDEF) is introduced to enhance spatial detail and high-frequency edge information expression; meanwhile, a Fourier jump enhancement module (FFSM) is adopted to jointly refine jump connection features in a spatial domain and a frequency domain. Through the synergistic effect of the modules, the network can realize continuous recovery and structural consistency modeling of a crack boundary in a complex pavement environment, so that the accuracy and the stability of a segmentation result are remarkably improved.

The invention relates to the technical field of medicine, and particularly discloses a semi-supervised medical image segmentation method based on an improved Transform, which designs a sphere embedded improved Transform module, constructs a boundary enhancement module based on morphological difference, and aims to improve the medical image segmentation performance. According to the method, a semi-supervised learning framework is adopted, and the dependence on a large-scale annotated data set is reduced by effectively utilizing limited annotated data and rich unannotated data, so that the accurate segmentation of the medical image is realized under the condition of limited resources. The method is expected to reduce the cost and time of data annotation while improving the segmentation precision, and brings a new breakthrough to the field of medical image segmentation.

The invention discloses an object three-dimensional reconstruction method, device and system based on deep learning. The reconstruction method comprises the following steps: acquiring a multi-view color image of an object through a controllable image acquisition device; reconstructing a sparse three-dimensional point cloud by using a motion recovery structure method and obtaining a camera pose; initializing parameters of the three-dimensional Gaussian sputtering model based on the sparse point cloud and performing training optimization; a target object semantic segmentation data set is constructed, and a low-rank adaptive technology is adopted to finely segment all models; generating prompts through an open vocabulary detection model at each view angle, obtaining an accurate segmentation mask, and optimizing a three-dimensional segmentation weight by adopting a joint loss function fusing color consistency loss and edge perception loss; and finally outputting the color three-dimensional point cloud of the target object. According to the method, the original image is segmented, so that the influence of the quality of the rendered image is avoided; the segmentation precision of the model in a specific scene is improved through field adaptive fine tuning; and the accuracy of the segmentation boundary is ensured by adopting a double-loss joint optimization mechanism.

According to the movie and television play table book extraction method and device, the storage medium and the computer equipment provided by the invention, after an audio and video file of a movie and television play is split into a video file and an audio file, feature recognition is performed on the video file to obtain a subtitle text, speaker face information and a video understanding text; performing voice understanding on the audio file to obtain a voice transcription text and a voice understanding text; wherein the voice transcription text can be corrected into the standard transcription text with high accuracy through the subtitle text. Therefore, based on the face information of the speaker, the line segment of each speaker in the standard transcriptional text and the audio and video file is aligned, so that speaker information with accurate segmentation and semantic coherence can be obtained; and then, through combination with a character side-writing text generated by side-writing analysis on the speaker based on the video, the voice understanding text and the speaker information, table book information is constructed, and related feature description of the character can be covered on the basis of containing the line content, so that the content and depth of the table book are enriched.

The invention discloses a camouflage object detection refining method based on an uncertainty mask Bernoulli diffusion model. The camouflage object detection refining method comprises the following steps: firstly, generating an initial segmentation mask by using a pre-training model; analyzing the image and the initial mask through a hybrid uncertainty quantization network (HUQNet), and generating a spatial uncertainty mask for identifying a residual region; taking the initial mask as a Bernoulli distribution mean value, modulating noise injection in combination with an uncertain mask, iteratively denoising through a Bernoulli diffusion model, and correcting a residual region in a targeted manner; and finally, fusing the refining result and the initial mask to determine an area, and outputting a final segmentation mask. The problems of large-range fuzzy edge, detail loss and false positive / negative correction existing in camouflage object detection in the prior art are solved. The camouflage object detection refinement device provided by the invention has wide application potential in multiple fields by improving the capability of accurately segmenting an object highly fused with the environment.

The invention discloses a method for estimating plant biomass based on map multi-modal feature extraction and fusion. The method comprises the following steps: acquiring a plant RGB image and a multi-spectral image; inputting the preprocessed RGB image and NIR wave band image into a double-model cooperation segmentation framework to realize image segmentation; binary image features, color features, texture features, reflectivity and the like are calculated, feature splicing is carried out, and high-dimensional features are constructed; carrying out dimension reduction on the high-dimensional features; and training a deep neural network through the effective features and the biomass to realize biomass estimation. According to the method, a zero sample learning-based double-model cooperation segmentation framework is utilized to realize accurate segmentation of a single plant on the premise that a large number of training sets are not needed; multi-modal feature information is extracted based on the segmented single plant image, an improved SHAP model is introduced to reduce the feature space dimension, and the inversion precision and the operation efficiency are improved while the information effectiveness is ensured; through a high-precision deep neural network model, rapid, lossless and accurate biomass acquisition is realized.

The invention discloses a brain tumor region segmentation method based on adaptive boundary guided aggregation SAM, and relates to the field of medical image analysis. The fusion output of the multi-modal magnetic resonance image is input into an image segmentation model to obtain image embedding, the boundary embedding of the brain tumor and the real boundary of the tumor are obtained through a boundary mining network, the boundary features are enhanced through an adaptive boundary enhancement model, the image embedding and the boundary embedding are output as fusion embedding by applying dynamic fusion, and the real boundary of the tumor is obtained. Inputting the fused embedded and adaptive enhanced boundary features and the features of the prompt encoder into a mask decoder of a segmentation model to obtain a segmentation result, and obtaining a total loss function in combination with a boundary mining network and a loss function based on region prediction; according to the brain tumor region segmentation method based on adaptive boundary guided aggregation SAM, accurate segmentation of the brain tumor region is realized.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention relates to the field of new energy, and discloses a photovoltaic system efficiency evaluation method and system based on unmanned aerial vehicle multi-source image fusion, and the method comprises the steps: collecting a visible light orthoimage, thermal imaging data and environment parameters of a photovoltaic system, and obtaining a registration result through employing an improved feature point matching algorithm; based on a registration result, combining spatial features of a visible light image and temperature features of thermal imaging, realizing accurate segmentation of the photovoltaic panel through a deep learning model, and identifying the type, the arrangement mode and the installation angle of the photovoltaic panel at the same time; establishing a mathematical model of the relation between the photovoltaic panel temperature distribution and the power generation efficiency, distinguishing the normal working temperature difference and the fault hot spot, and analyzing and determining the efficiency attenuation degree and the fault type of the photovoltaic system through a heat distribution mode. The method is accurate in image registration, good in data fusion effect, accurate in temperature anomaly detection and comprehensive in efficiency evaluation, and provides more efficient and accurate technical support for management and maintenance of the distributed photovoltaic system.

The invention discloses an intelligent bone marrow megakaryocyte morphology detection method which comprises the following steps: S1, data preparation: collecting and preprocessing a digital large map of a bone marrow smear, labeling megakaryocytes, and establishing a labeled sample; s2, generating and sorting a sample: extracting a small graph sample by taking megakaryocyte as a center; s3, constructing a deep learning model: constructing and training a deep convolutional neural network, and performing model optimization and performance improvement by using the generated small image sample set; s4, large image detection and reasoning: calling the trained model for reasoning by adopting a sliding window mechanism, and fusing detection results of a plurality of small windows back to an original large image through confidence weighting and a non-maximum suppression strategy; and S5, target cell segmentation: carrying out target segmentation on the detected megakaryocyte, and introducing a pyramid structure for cells with different sizes to obtain an accurate segmentation mask of each target cell. According to the method, the megakaryocyte detection and segmentation precision is improved through large image labeling, small image training and a sliding window reasoning strategy.

The invention belongs to the technical field of conveyor coal flow monitoring, and aims at solving the problem that an existing coal flow monitoring method is insufficient in precision. According to the belt conveyor coal flow monitoring method based on the inspection robot and the monocular vision, coal flow images are dynamically collected through the inspection robot, accurate segmentation of a coal flow area is achieved through a deep learning algorithm, and a coal flow three-dimensional point cloud is reconstructed in combination with a monocular depth estimation technology; and finally, qualitative and quantitative analysis of the coal flow is realized. Compared with a traditional method, the method has the advantages of non-contact measurement, full conveying belt coverage, high calculation efficiency, adaptability to complex environments and the like, and reliable data support can be provided for a coal mine intelligent transportation system.

The invention discloses an image contour recognition method and device for a cell cluster, and relates to the technical field of biological image processing, and the method comprises the steps: converting an input cell cluster image into a binary image; carrying out expansion operation and distance transformation on the image after morphological operation, and carrying out subtraction operation on the two obtained images to obtain a third image containing an unknown region; detecting connected regions in the accurate foreground region, and marking each independent connected region, background region and unknown region to obtain a marked image; performing region segmentation on the original image to obtain segmented marked regions, generating a corresponding binary mask image, and extracting the contour of each marked region by using the binary mask image; identifying and filtering the contour of each marked area to obtain an effective contour; the size of each effective profile is converted into an equivalent sphere diameter. According to the invention, accurate segmentation of adjacent or adhesion areas in the image can be efficiently realized, and accurate counting and size measurement of cell clusters can be obtained.

The invention provides a liver and tumor recognition method and device fusing an attention mechanism and multi-scale convolution and a readable storage medium, and aims to solve the problems of insufficient global information capture, poor multi-scale feature adaptation, weak spatial perception and the like in the existing medical image segmentation technology. A convolution attention module and a coordinate attention module are embedded in jump connection to enhance key feature focusing, and a multi-scale down-sampling block is designed to adapt to tumor multi-scale features. Through data preprocessing and model training and optimization, accurate segmentation of the liver and the tumor is realized. Experiments show that the method significantly improves the segmentation precision in public and clinical data sets, especially improves the detection rate of small tumors and the boundary integrity of large tumors, and provides reliable support for clinical diagnosis.

The invention provides a polyp image segmentation method based on multi-branch fusion and cross attention collaboration, belongs to the technical field of medical image segmentation, solves the problem of high omission ratio of small polyps by introducing an encoder structure based on Pvt v2 and combining multi-module collaboration, meets the requirement for precise detection of the small polyps clinically, and improves the detection accuracy of the small polyps. The early screening effect of the colorectal cancer is improved; by designing a multi-branch fusion module, the problem of fuzzy polyp edge areas is solved, the accuracy of segmentation results is improved, and an accurate basis is provided for judging polyp boundaries; the balance between the calculation efficiency and the feature capture capability is realized by providing a double-channel cross attention module, and the efficient and accurate segmentation of the complex polyp is ensured.

The invention discloses a landslide segmentation model based on multi-loss function fusion, particularly relates to the field of landslide monitoring, is used for solving the problems of precise segmentation of a landslide area and insufficient identification precision in a complex scene, realizes precise segmentation of a landslide image through multi-level feature extraction and adaptive optimization, and has remarkable beneficial effects. The detail identification of the landslide area is enhanced by utilizing self-adaptive channel response and multi-scale feature fusion, so that features of different scales are fully expressed; weighted combination and residual connection of global and local features ensure continuous transmission and integration of high-level and low-level features, and detail and global information are completely reserved. The spatial resolution is recovered through up-sampling and channel splicing, and it is ensured that a segmentation result is consistent with an original image; the multi-level nonlinear loss function improves the robustness of the model under the condition that the samples are unbalanced, samples difficult to classify are effectively concerned, and the model shows a high-precision segmentation effect in a complex scene.

The invention discloses a field wheat three-dimensional character analysis method based on unmanned aerial vehicle multi-view three-dimensional point cloud, and the method comprises the following steps: S1, shooting a multi-view image and carrying out the three-dimensional reconstruction, and obtaining an original point cloud; performing interpolation segmentation and secondary segmentation, and outputting a plurality of cell point clouds; s2, performing data enhancement, starting model training, and obtaining each 3D bounding box; s3, extracting point cloud data of each planting row from each 3D bounding box by using a row segmentation algorithm, and calculating each static phenotype parameter; and S4, calculating five dynamic phenotypic parameters of the field wheat. According to the invention, based on the 3D wheat plot detection network and the row segmentation algorithm, the point cloud data and the 3D bounding box of the wheat are rapidly extracted, and accurate monitoring and accurate segmentation of the wheat are realized; based on accurate analysis of static phenotypic parameters, a new system of dynamic phenotypic parameters is provided, and based on the dynamic phenotypic parameters, the growth vigor and yield of wheat can be effectively predicted.

The invention discloses a CT-based osteoporosis intelligent diagnosis method and system, and relates to the technical field of medical image diagnosis, and the method comprises the steps: carrying out the positioning of a lumbar region in an abdomen or thoracic and abdominal CT image through a deep learning model; digitally modeling the shape and the internal structure of the vertebral body; and automatic diagnosis of osteoporosis is completed. According to the method, full-automatic positioning, accurate segmentation and intelligent diagnosis of osteoporosis of the lumbar region in a conventional abdomen or thoracic and abdominal CT image are realized, the accuracy and efficiency of diagnosis are remarkably improved, and the problems of strong subjectivity and poor repeatability caused by dependence on manual sketching or simple threshold segmentation in a traditional method are solved; the method can be directly operated based on clinical conventional CT data, is beneficial to large-scale osteoporosis screening and early intervention, and has remarkable clinical popularization significance and technical progress value.