Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

ABSTRACT:

Purpose

Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer.

Methods

Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs)

SUBMITTER: Jeong S 

PROVIDER: S-EPMC11290636 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature