Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

ABSTRACT:

Background and purpose

The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. Delineation is a time-consuming process that is subject to inter- and intra-observer variability. While methods have been proposed to predict GTV contours, typical approaches ignore variability and therefore fail to utilize the valuable confidence information offered by multiple contours.

Materials and methods

In this work we propose an automatic GTV contouring method for soft-tissue sarcomas from X-ray computed tomography (CT) images, using deep learning by integrating inter- and intra-observer variability in the learned model. Si

SUBMITTER: Marin T 

PROVIDER: S-EPMC8934266 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature