Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy.

ABSTRACT:

Purpose

The delineation of organs at risk (OARs) is fundamental to cone-beam CT (CBCT)-based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning-based rapid multiorgan delineation method for use in CBCT-guided adaptive pancreatic radiotherapy.

Methods

To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study. First, instead of directly segmenting organs on CBCT images, a pretrained cycle-consistent generative adversarial network (cycleGAN) was applied to generating synthetic CT images given CBCT images. Second, an advanced deep learning model called mask-scoring regional convolutional neural network (MS R-CNN) was applied on thos

SUBMITTER: Dai X 

PROVIDER: S-EPMC8595847 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature