Deep-learning-based segmentation of organs-at-risk in the head for MR-assisted radiation therapy planning

László Ruskó, Marta E. Capala, Vanda Czipczer*, Bernadett Kolozsvári, Borbála Deák-Karancsi, Renáta Czabány, Bence Gyalai, Tao Tan, Zoltán Végváry, Emőke Borzasi, Zsófia Együd, Renáta Kószó, Viktor Paczona, Emese Fodor, Chad Bobb, Cristina Cozzini, Sandeep Kaushik, Barbara Darázs, Gerda M. Verduijn, Rachel PearsonRoss Maxwell, Hazel McCallum, Juan A. Hernandez Tamames, Katalin Hideghéty, Steven F. Petit, Florian Wiesinger

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

5 Citations (Scopus)

Abstract

Segmentation of organs-at-risk (OAR) in MR images has several clinical applications; including radiation therapy (RT) planning. This paper presents a deep-learning-based method to segment 15 structures in the head region. The proposed method first applies 2D U-Net models to each of the three planes (axial, coronal, sagittal) to roughly segment the structure. Then, the results of the 2D models are combined into a fused prediction to localize the 3D bounding box of the structure. Finally, a 3D U-Net is applied to the volume of the bounding box to determine the precise contour of the structure. The model was trained on a public dataset and evaluated on both public and private datasets that contain T2-weighted MR scans of the head-and-neck region. For all cases the contour of each structure was defined by operators trained by expert clinical delineators. The evaluation demonstrated that various structures can be accurately and efficiently localized and segmented using the presented framework. The contours generated by the proposed method were also qualitatively evaluated. The majority (92%) of the segmented OARs was rated as clinically useful for radiation therapy.

Original languageEnglish
Title of host publicationBIOIMAGING 2021 - 8th International Conference on Bioimaging; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
EditorsAlexandre Douplik, Ana Fred, Hugo Gamboa
Pages31-43
Number of pages13
ISBN (Electronic)9789897584909
DOIs
Publication statusPublished - 2021
Event8th International Conference on Bioimaging, BIOIMAGING 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 - Virtual, Online
Duration: 11 Feb 202113 Feb 2021

Publication series

SeriesBIOIMAGING 2021 - 8th International Conference on Bioimaging; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021

Conference

Conference8th International Conference on Bioimaging, BIOIMAGING 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
CityVirtual, Online
Period11/02/2113/02/21

Bibliographical note

Funding Information:
This research is part of the Deep MR-only Radiation Therapy activity (project numbers: 19037, 20648) that has received funding from EIT Health. EIT Health is supported by the European Institute of Innovation and Technology (EIT), a body of the European Union receives support from the European Union´s Horizon 2020 Research and innovation programme.

Publisher Copyright: © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

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