Automated and robust organ segmentation for 3D-based internal dose calculation.

ABSTRACT:

Purpose

In this work, we address image segmentation in the scope of dosimetry using deep learning and make three main contributions: (a) to extend and optimize the architecture of an existing convolutional neural network (CNN) in order to obtain a fast, robust and accurate computed tomography (CT)-based organ segmentation method for kidneys and livers; (b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; and (c) to evaluate dosimetry results obtained using automated organ segmentation in comparison with manual segmentation done by two independent experts.

Methods

We adapted a performant deep learning approach using CT-images to delineate organ boundaries with sufficiently high accuracy and adequate processing time. The segmente

SUBMITTER: Nazari M 

PROVIDER: S-EPMC8184901 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature