Hip joint X-ray image segmentation method and system based on local vision clue
A technology of visual cues and light images, applied in the field of deep learning and computer vision, can solve limited problems, achieve the effect of reducing complexity, model robustness, and improving model effect
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[0028] The present invention will be further described below in conjunction with the accompanying drawings of the specification.
[0029] figure 1 It is a schematic flowchart of a hip joint X-ray image segmentation method based on local visual cues provided by an embodiment of the present invention. The method includes the following steps:
[0030] Step 101: Obtain a data set labeled by X-ray images, preprocess the image data in the data set to obtain a hip joint region (ROI) based on segmentation and labeling, and normalize the ROI region;
[0031] Specifically, when preprocessing the image data in the data set, you can first use the labelme annotation tool to obtain the X-ray image annotation data set, convert the data set format to COCO2014 format, and use the pycocotools toolkit to deserialize the json to obtain the network Input, recalculate the ROI area according to the range of the divided points and random numbers, enhance the data, and normalize it to 128x128.
[0032] Step 1...
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