Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.

ABSTRACT:

Background and purpose

Accurate and automated segmentation of targets and organs-at-risk (OARs) is crucial for the successful clinical application of online adaptive radiotherapy (ART). Current methods for cone-beam computed tomography (CBCT) auto-segmentation face challenges, resulting in segmentations often failing to reach clinical acceptability. Current approaches for CBCT auto-segmentation overlook the wealth of information available from initial planning and prior adaptive fractions that could enhance segmentation precision.

Materials and methods

We introduce a novel framework that incorporates data from a patient's initial plan and previous adaptive fractions, harnessing this additional temporal context to significantly refine the segmentation accuracy for the current

SUBMITTER: Zhao H 

PROVIDER: S-EPMC11315102 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature