Automated Quality Assessment of Liver Magnetic Resonance Images with Fully Automatic Segmentation and Radiomics Approach

This study investigated the feasibility and performance of quality assessment of hepatic magnetic resonance (MR) images using a deep-learning-based segmentation and radiomics approach. We used a pre-trained deep learning model to segment the liver on different contrast-enhanced MR phases and then extracted quantitative features to assess the image quality by a machine learning method. The results showed that the radiomics model had a high performance for image quality identification in both training and test sets. This suggests that it was feasible to automate the identification of image quality by using radiomics approaches.