Deep learning-based bowel automatic segmentation and visualisation of Crohn's disease using multilabelled continuous MRE images
Author Block: L. Huang1, Z. Zhong2, B. Huang2, S-T. Feng1, X. Li1; 1Guangzhou/CN, 2Shenzhen/CN
Purpose: Recognition of bowel segments from magnetic resonance enterography (MRE) images is quite challenging and time-consuming due to unclear boundary, shape, size, and appearance variations. We established a publicly available whole bowel segments MR data set with benchmark results and visualisation.
Methods or Background: We retrospectively collected T2-weighted coronal MRE data from 70 patients with Crohn's disease (CD). The bowel images per patient were divided into ten segments (stomach, duodenum, small intestine, appendix, caecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel level annotations labelled by two experienced radiologists. Then, nnU-Net model, a deep learning-based segmentation method that automatically configures all hyperparameters based on the data set characteristics, was employed on this data set (training set, n=56; test set, n=14). To reinforce the mutually exclusive relationship between tags, a topological interaction loss function was utilised. The segmentation algorithm was assessed using the dice similarity coefficient (DSC).
Results or Findings: Evaluating the performance of bowel segmentation, the mean DSC in the test set was 0.778. Our nnU-Net method in segmenting digestive tract can achieve DSC of 0.963 ± 0.042 in stomach, 0.886 ± 0.049 in duodenum, 0.936 ± 0.024 in small intestine, 0.378 ± 0.441 in appendix, 0.598 ± 0.294 in cecum, 0.825 ± 0.131 in ascending colon, 0.819 ± 0.231 in transverse colon, 0.819 ± 0.186 in descending colon, 0.801 ± 0.220 in sigmoid colon and 0.859 ± 0.130 in rectum, respectively. Segmentation results with predicted bowel boundary can be shown by two- or three-dimensional visual representation.
Conclusion: We presented a new data set containing labels for all digestive tract segments on MRE images. Accurate deep learning-based bowel automatic segmentation and visualisation of CD can facilitate the application of artificial intelligence in CD.
Limitations: No limitations were identified.
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Not applicable
Ethics committee - additional information: No information provided by the submitter.