Health image data analysis processing assisted diagnosis method

Abstract

The present application relates to the field of health care informatics, and discloses a health image data analysis processing auxiliary diagnosis method. The initial deformation field is predicted by the feature encoder and the deformable convolution layer through the acquisition of the to-be-registered image and the reference image of the same subject at different time points. The local gray histogram features of the two are extracted to calculate the gray distribution offset, which is converted into a contrast compensation coefficient. In the back propagation stage, the contrast compensation coefficient is applied to the initial deformation field gradient, and the contrast adaptive correction is performed on the spatial displacement vector to generate the target deformation field. The spatial transformation of the to-be-registered image is performed by using the target deformation field, the pathological evolution features are extracted by inputting the time difference network, and the disease progression evaluation result is output. The present scheme corrects the deviation of the spatial displacement vector of the deformation field caused by the inconsistent gray, eliminates the registration misplacement caused by the difference of the acquisition parameters, and ensures the accuracy of the pathological evolution feature extraction.

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