Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

ABSTRACT: Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency. This study includes a real-world cohort of 394 patients who each received radiation therapy (mainly for lung cancer). Segmentation of the esophagus was performed by 8 physicians as part of routi

SUBMITTER: Yan Y 

PROVIDER: S-EPMC10827712 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature