Deep learning for automatic volumetric bowel segmentation on body CT images.

Objectives: To develop a deep neural network for automatic bowel segmentation and assess its applicability for estimating large bowel length (LBL) in individuals with constipation.

Materials And Methods: We utilized contrast-enhanced and non-enhanced abdominal, chest, and whole-body CT images for model development. External testing involved paired pre- and post-contrast abdominal CT images from another hospital. We developed 3D nnU-Net models to segment the gastrointestinal tract and separate it into the esophagus, stomach, small bowel, and large bowel. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC) based on radiologists' segmentation. We employed the network to estimate LBL in individuals having abdominal CT for health check-ups, and the height-corrected LBL was compared between groups with and without constipation.

Results: One hundred thirty-three CT scans (88 patients; age, 63.6 ± 10.6 years; 39 men) were used for model development, and 60 for external testing (30 patients; age, 48.9 ± 15.8 years; 16 men). In the external dataset, the mean DSC for the entire gastrointestinal tract was 0.985 ± 0.008. The mean DSCs for four-part separation exceeded 0.95, outperforming TotalSegmentator, except for the esophagus (DSC, 0.807 ± 0.173). For LBL measurements, 100 CT scans from 51 patients were used (age, 67.0 ± 6.9 years; 59 scans from men; 59 with constipation). The height-corrected LBL were significantly longer in the constipation group on both per-exam (79.1 ± 12.4 vs 88.8 ± 15.8 cm/m, p = 0.001) and per-subject basis (77.6 ± 13.6 vs 86.9 ± 17.1 cm/m, p = 0.04).

Conclusion: Our model accurately segmented the entire gastrointestinal tract and its major compartments from CT scans and enabled the noninvasive estimation of LBL in individuals with constipation.

Key Points: Questions Automated bowel segmentation is a first step for algorithms, including bowel tracing and length measurement, but the complexity of the gastrointestinal tract limits its accuracy. Findings Our 3D nnU-Net model showed high performance in segmentation and four-part separation of the GI tract (DSC > 0.95), except for the esophagus. Clinical relevance Our model accurately segments the gastrointestinal tract and separates it into major compartments. Our model potentially has use in various clinical applications, including semi-automated measurement of LBL in individuals with constipation.