Segmentation by test-time optimization for CBCT-based adaptive radiation therapy.

ABSTRACT:

Purpose

Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images, which often have severe artifacts and lack soft-tissue contrast, making direct segmentation very challenging. Propagating expert-drawn contours from the pretreatment planning CT through traditional or deep learning (DL)-based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, and so they may be affected by the generalizability problem.

Methods

In this paper, we propose a method called test-time optimization (TTO) to refine a pretrained DL-base

SUBMITTER: Liang X 

PROVIDER: S-EPMC10121749 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature