We previously studied the application of a contrast-agnostic approach for MRI/CT abdomen organs segmentation, based on the generation of synthetic data. This synthetic data was then further used as a training set for a fully-supervised U-Net network. Since this study was performed, other methods aiming to segment MR abdominal organs have been published. Our goal is to evaluate these new methods on IDC MR abdominal-focused data and see how it compares to our method.
Comparison of MR segmentation methods on a subject from AMOS dataset:
Top left = ground truth expert segmentations, top right = our approach
Bottom left = TotalSegmentator, bottom middle = MRSegmentator, bottom right = our approach
Dice distributions between AI segmentations and expert annotations on AMOS22 MR training split.