The invention belongs to the technical field of medical
image processing and
computer vision, and particularly relates to an
MRI image liver fibrosis automatic grading method based on imaging
omics analysis. The invention particularly relates to an imaging
omics analysis method for grading magnetic
resonance dynamic enhanced imaging
liver fibrosis of a Gd-EOB-DTPA contrast agent. The method comprises the following steps: establishing an initial
data set by taking a Gd-EOB-DTPA dynamic enhanced image of a
hepatitis B patient as
source data; registering the initial data; carrying out automatic liver segmentation by utilizing transfer learning to obtain an ROI; performing image
omics analysis: for the ROI, carrying out image omics
feature extraction and
feature screening on a plurality of DCEsequences to obtain a feature subset of important features, and carrying out training of a plurality of classifiers to select an optimal classifier; and finally, predicting and evaluating the classification performance in an external
test set by using the classifier. According to the method, the accuracy and reliability of automatic
liver fibrosis grading based on DCE images can be effectively improved, and meanwhile, the time and energy of clinicians are saved to a great extent.