8results about How to "Realize automatic segmentation" patented technology

The invention relates to a three-stage tooth intelligent accurate segmentation method based on a shape distance field, and the method comprises the following steps: S1, obtaining a tooth image, and carrying out the preprocessing; s2, extracting a region of interest, and determining a foreground region; s3, calculating the distance and direction from each foreground pixel in the foreground region to the centroid of the corresponding tooth to form a centroid offset vector; s4, generating a tooth centroid density map, and performing clustering identification on centroids of the teeth; s5, designing a segmentation mask loss function and a segmentation boundary loss function of the model; s6, designing a shape loss function based on a distance field; and the trained tooth segmentation model comprises a multi-label mask segmentation sub-network, a multi-label boundary segmentation sub-network and a shape sub-network based on a distance field, which are respectively used for executing a mask image generation task, a boundary detection task and a boundary fine description task, and outputting a final segmentation result. Compared with the prior art, the method has the advantages of improving the accuracy of tooth segmentation and the like.

The invention provides a camera offset detection method and system based on image segmentation, and belongs to the technical field of image processing, and the method comprises the steps: inputting a to-be-detected railway image into an image segmentation model, and obtaining a plurality of rail region contours; a target contour is determined from all the rail area contours, and the target contour is divided into a plurality of convex areas in the preset direction when it is judged that concave points exist in the target contour; determining a target area from all the convex areas, and performing straight line fitting on contour points in the target area to obtain a straight line to be detected; and when it is determined that the camera deviates based on a comparison result of the to-be-detected straight line and the reference straight line, alarm information of camera deviation is generated. According to the invention, through image segmentation and contour analysis, the rail boundary is accurately extracted and the camera offset is detected, so that the accuracy of camera offset detection is effectively improved.

The invention belongs to the technical field of photovoltaic module recovery, and particularly relates to a device for segmenting photovoltaic glass and an EVA adhesive layer and a segmentation process. A photovoltaic glass heating area is used for heating the segmented photovoltaic glass and conveying the heated photovoltaic glass to be segmented to a photovoltaic glass segmentation area; the photovoltaic glass dividing area comprises an unpowered conveying roller, a pushing mechanism, a conveying belt, a dividing mechanism and a recycling box, and the pushing mechanism is used for moving the photovoltaic glass to be divided from the input end to the output end and conveying the divided photovoltaic glass to the conveying belt; an included angle is formed between a cutter of the cutting mechanism and the photovoltaic glass to be cut during cutting, and the included angle is 0-30 degrees. The photovoltaic glass cutting device has the function of heating photovoltaic glass to be cut, meanwhile, the angle of the cutter when the photovoltaic glass to be cut is cut is controlled, and the effect of removing an EVA adhesive layer is improved.

This invention provides a deep learning-based intelligent analysis method for cardiovascular medical images, comprising the following steps: S1, heterogeneous data fusion and acquisition of multimodal medical images and physiological signals; S2, intelligent extraction and quantitative analysis of vascular structural features based on deep learning; S3, hemodynamic parameter modeling and risk region segmentation based on fused structural features; S4, texture feature mining and intelligent identification of lesion types in high-risk areas; S5, cardiovascular disease risk stratification prediction based on multi-dimensional feature fusion; S6, federated learning optimization and closed-loop update of the model. This invention's deep learning-based intelligent analysis method for cardiovascular medical images achieves, for the first time, heterogeneous fusion of medical images, millimeter-wave radar-PPG physiological signals, and clinical molecular data, and achieves precise spatiotemporal alignment through GPS-controlled crystal oscillators, overcoming the limitations of single image analysis and providing a more comprehensive feature foundation for subsequent feature extraction and risk prediction.

A method for automatic segmentation of the temporal bone inner ear bone cavity structure based on a coarse-to-fine dense encoding and decoding network belongs to the field of medical imaging. The present invention adopts a coarse-to-fine framework to first perform coarse segmentation on the anatomical structure to be segmented in the temporal bone region and calculate the coordinates of the center point. Around the center point, the image is expanded to the outside to a region that can completely contain the inner ear bone cavity structure, and a part of the background information can be retained as a sub-region for further precise segmentation. In the fine segmentation stage, dense connection modules are introduced in the encoding process to extract more sufficient features, and hollow convolutions are added to the dense connection modules, so that the segmentation algorithm obtains a larger receptive field for the segmentation target, so as to extract more sufficient surrounding features and spatial information. In the decoding stage, the features extracted in the encoding stage are upsampled by transposed convolution, and after each transposed convolution, a dense connection module is introduced to enhance the reuse of the decoded information. The segmentation of the present invention is more accurate.

The invention discloses a transplanting planter, and relates to the technical field of planting equipment, and the transplanting planter comprises a vehicle body, a soil opening mechanism, a soil covering mechanism, a material warehouse mechanism, a conveying mechanism, a circulating feeding mechanism, a cutting mechanism, a receiving funnel and a seedling conveying and transplanting mechanism. According to the technical scheme, by moving the vehicle body, the soil digging mechanism digs planting furrows in the ground; the material warehouse mechanism can sequentially feed seedling raising trays to a discharging port, the seedling raising trays are divided into single seedling raising pots through the dividing mechanism, and the conveying mechanism can sequentially convey the single seedling raising pots to an outlet of the conveying mechanism. The seedling feeding and transplanting mechanism controls the receiving funnel to move and extend into the planting ditch, and the seedling raising plate falls into the planting ditch; the soil covering mechanism is used for covering soil on the planting furrows; the seedling raising tray can be automatically segmented and planted on the whole, and the problem that the labor intensity is high during seedling planting is solved.

The application relates to a grinding area planning method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining medical image data of a hip joint part of a target object; inputting the medical image data into a pre-trained deep learning model to obtain bone tissue data and position information of a hip socket center point output by the deep learning model; generating a pelvis model to be ground based on the bone tissue data; determining a grinding area of the pelvis model according to the pelvis model, the position information and a preset acetabular cup model, and visually displaying the pelvis model and the grinding area. Through the application, the problem that manual segmentation based on pelvis tissue depends on the experience and proficiency of operators in the related art, resulting in low segmentation efficiency and low segmentation accuracy, is solved, automatic segmentation of medical image data based on a pre-trained deep learning model is realized, the segmentation accuracy and efficiency can be improved, and the pelvis model and the grinding area can be visually displayed.

The present application relates to a medical image segmentation method and a computer-readable storage medium, the method comprising: obtaining each organ image in the image to be segmented; inputting each organ image into the corresponding organ abnormal point segmentation model respectively, and obtaining the abnormal point segmentation result corresponding to each organ image; wherein the organ abnormal point segmentation model comprises a cascade structure of multiple segmentation networks, and the input data of the latter segmentation network comprises at least one output result of the previous segmentation network; according to the abnormal point segmentation result corresponding to each organ image, the whole body abnormal point segmentation result corresponding to the image to be segmented is obtained. The accuracy of the whole body abnormal point segmentation result obtained in this method can be greatly improved.