Automated Segmentation of the Small Bowel and Body Composition on MR Enterography: initial experience

Motivation: Deep-Learning-based techniques can improve interpretation of Magnetic Resonance Enterography (MRE) in the assessment of Crohn's Disease (CD). Goal(s): This initial study aimed to develop models for automated segmentation of the small bowel and segmentation and quantification of body fat and iliopsoas muscle content. Approach: Axial SSFSE-T2WI images were retrospectively collected from 115 initial subjects with suspected CD and negative MRE. 3D/2D nnU-Net models were trained to segment the small bowel and quantify fat and muscle at the L3 level. Results: 3D nnU-Net showed very good performance in segmentation of the small bowel, while 2D nnU-Net showed good/excellent segmentation performance for intra-abdominal fat/iliopsoas muscles. Impact: These results lay a foundation for the application of DL models to CD patients with small bowel involvement, both in automated detection of affected bowel segments and in accurate determination of body composition, potentially providing outcome information in these patients.