3results about How to "Realize automatic segmentation" patented technology

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.

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.