A comprehensive dataset of magnetic resonance enterography images with bowel segment annotations

Inflammatory bowel disease (IBD) is a kind of recurrent bowel disease and usually requires magnetic resonance enterography (MRE) examinations for diagnosis and monitoring. However, radiologists’ recognition of bowel segments from MRE images is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual efforts and provide automated tools to assist in the management of disease, but it requires a large-scale fine-annotated dataset for training. To address this gap, we collected MRE data from 114 IBD patients. The bowel images per patient were contoured and annotated as ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Further, we validated the efficiency of several state-of-the-art segmentation methods on this dataset. This work established a high quality, publicly available whole bowel segment MR dataset with benchmark results and laid a groundwork for IBD’s AI research.