Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.
Journal:
Medical physics
Published Date:
May 14, 2021
Abstract
PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ-at-risk (OAR) locations and shapes and to compute delivered dose. This study describes the development and evaluation of a deep-learning (DL) registration model to predict OAR segmentations on the CBCT derived from segmentations on the planning CT.