ePrints

Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

Lookup NU author(s): Dr Rachel Pearson, Dr Ross Maxwell, JJ Wyatt, Dr Hazel McCallum

Downloads

• Published version [.pdf]

Licence

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0).

Abstract

Copyright © 2023 Czipczer, Kolozsvári, Deák-Karancsi, Capala, Pearson, Borzási, Együd, Gaál, Kelemen, Kószó, Paczona, Végváry, Karancsi, Kékesi, Czunyi, Irmai, Keresnyei, Nagypál, Czabány, Gyalai, Tass, Cziria, Cozzini, Estkowsky, Ferenczi, Frontó, Maxwell, Megyeri, Mian, Tan, Wyatt, Wiesinger, Hideghéty, McCallum, Petit and Ruskó.Introduction: The excellent soft-tissue contrast of magnetic resonance imaging (MRI) is appealing for delineation of organs-at-risk (OARs) as it is required for radiation therapy planning (RTP). In the last decade there has been an increasing interest in using deep-learning (DL) techniques to shorten the labor-intensive manual work and increase reproducibility. This paper focuses on the automatic segmentation of 27 head-and-neck and 10 male pelvis OARs with deep-learning methods based on T2-weighted MR images. Method: The proposed method uses 2D U-Nets for localization and 3D U-Net for segmentation of the various structures. The models were trained using public and private datasets and evaluated on private datasets only. Results and discussion: Evaluation with ground-truth contours demonstrated that the proposed method can accurately segment the majority of OARs and indicated similar or superior performance to state-of-the-art models. Furthermore, the auto-contours were visually rated by clinicians using Likert score and on average, 81% of them was found clinically acceptable.

Publication metadata

Author(s): Czipczer V, Kolozsvari B, Deak-Karancsi B, Capala ME, Pearson RA, Borzasi E, Egyud Z, Gaal S, Kelemen G, Koszo R, Paczona V, Vegvary Z, Karancsi Z, Kekesi A, Czunyi E, Irmai BH, Keresnyei NG, Nagypal P, Czabany R, Gyalai B, Tass BP, Cziria B, Cozzini C, Estkowsky L, Ferenczi L, Fronto A, Maxwell R, Megyeri I, Mian M, Tan T, Wyatt J, Wiesinger F, Hideghety K, McCallum H, Petit SF, Rusko L

Publication type: Article

Publication status: Published

Journal: Frontiers in Physics

Year: 2023

Volume: 11

Online publication date: 19/09/2023

Acceptance date: 08/09/2023

Date deposited: 01/11/2023

ISSN (electronic): 2296-424X

Publisher: Frontiers Media SA

URL: https://doi.org/10.3389/fphy.2023.1236792

DOI: 10.3389/fphy.2023.1236792

Altmetrics

Funding

Share