Uncertainty-guided pancreatic tumor auto-segmentation with Tversky ensemble.

ABSTRACT:

Background and purpose

Pancreatic gross tumor volume (GTV) delineation is challenging due to their variable morphology and uncertain ground truth. Previous deep learning-based auto-segmentation methods have struggled to handle tasks with uncertain ground truth and have not accommodated stylistic customizations. We aim to develop a human-in-the-loop pancreatic GTV segmentation tool using Tversky ensembles by leveraging uncertainty estimation techniques.

Material and methods

In this study, we utilized a total of 282 patients from the pancreas task of the Medical Segmentation Decathlon. Thirty patients were randomly selected to form an independent test set, while the remaining 252 patients were divided into an 80-20 % training-validation split. We incorporated Tversky loss laye

SUBMITTER: Yu C 

PROVIDER: S-EPMC12019452 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature