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

ABSTRACT:

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 heigh

SUBMITTER: Park J 

PROVIDER: S-EPMC12559117 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature