MRI image liver fibrosis automatic grading method based on imaging omics analysis

Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of medical image processing and computer vision, in particular to a method for automatic grading of liver fibrosis in MRI images. Background technique [0002] Liver fibrosis is a common pathological process in a variety of chronic liver diseases and is regarded as the liver's response to liver injury due to various reasons. The proliferation of hepatic stellate cells will produce a large amount of extracellular matrix, which will be deposited in the extravascular space, eventually causing the formation of liver fibrosis (Bataller, R. and D.A. Brenner, Liver fibrosis. J Clin Invest, 2005. 115 (2 ): p. 209-18.). After antiviral and antifibrotic treatment, liver fibrosis can be reversed, and even early cirrhosis may be reversed. Studies have shown that patients with liver fibrosis Scheuer-Ludwig≥S2 need intervention therapy (EASL-ALEH Clinical Practice Guidelines: Non-invasive tests forevaluation of liver di...

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Problems solved by technology

[0005] At present, most methods for automatic grading of liver fibrosis through images are only analyzed on a specific first-stage image, and only by comparing some low-level image features such as gray values ​​or combining a small number of radiomics features based on previous experience ( Kato, H., et al., Computer-aided diagnosis of hepatic fibrosis:preliminary evaluation of MRI texture analysis using the finite difference method and an artificial neural network. AJR Am J Roentgenol, 2007. 189(1):p. 117-22 ,Duan, J., et al., Microcomputed tomography with diffraction-enhanced imaging for morphologic characterization and quantitative evaluation of microvessel of hepatic fibrosis in rats. PLoS One, 2013. 8(10): p. e78176,Wu, Z., et al. al., Hepatitis C related chronic liver cirrhosis: feasibility oftexture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade. PLoS One, 2015. 10(3): p. e0118297. etc.), such methods are difficult to find and Radiomics features most relevant to fibrosis grade

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