Method for automatically generating scanning area in CT system
A scanning area and automatic generation technology, which is applied in the field of medical imaging, can solve the problems of inaccurate scanning area, increased workload of scanning technicians, and long time, so as to achieve the effect of reducing workload, shortening preparation time, and improving preparation time
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Embodiment 1
[0046] Embodiment 1: In this embodiment, a camera installed directly above the CT bed is used to collect images of patients on the bed. When collecting images, the camera shoots vertically downward, and the collected images are sent to the computer system of the CT system for image analysis and processing. .
[0047] In this embodiment, the target detection model based on the convolutional neural network is used to process the input image, and the target detection model based on the convolutional neural network is an RCNN structure, such as image 3 As shown, it includes a candidate region extraction network, a CNN feature extractor, a classifier and a regressor; the candidate region extraction network generates a candidate frame according to the input image and adjusts the size of the candidate frame to a specified size, and the CNN feature extractor extracts each The feature vector of the candidate frame, the extracted feature vector is sent to the classifier for classificat...
Embodiment 2
[0059] Embodiment 2: In this embodiment, a camera installed directly above the CT bed is used to collect images of patients on the bed. When collecting images, the camera shoots vertically downward, and the collected images are sent to the computer system of the CT system for image analysis and processing. .
[0060] In this embodiment, the image segmentation model based on the convolutional neural network is used to process the input image, and the image segmentation model based on the convolutional neural network is a UNet structure, such as Figure 4 As shown, it includes: an input module, a feature extraction module, an upsampling module and an output module, wherein the input module inputs multimodal image data (CT positioning phase data and images taken by the camera are registered and cropped to the same size image data) , the feature extraction module consists of cascaded convolution and pooling operations to extract input features; the upsampling module consists of up...
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