Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

ABSTRACT:

Background

Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.

Methods

Twenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Conto

SUBMITTER: Urago Y 

PROVIDER: S-EPMC8427857 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature