Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

ABSTRACT:

Background

Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant. However, the majority of the studies investigated automatic contouring based on computed tomography (CT).

Purpose

In this study, we investigate the feasibility of clinical use of deep learning-based automatic OARs delineation on MRI.

Materials and methods

We included 150 patients diagnosed with prostate cancer who underwent MR-only radiotherapy. A three-dimensional (3D) T1-weighted dual spoiled gradient-recalled

SUBMITTER: Savenije MHF 

PROVIDER: S-EPMC7216473 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature