4.6 Article

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

Journal

FRONTIERS IN PHYSICS
Volume 11, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fphy.2023.1236792

Keywords

organs-at-risk segmentation; head-and-neck; pelvis; MRI; deep learning; U-Net

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This study focuses on the automatic segmentation of head-and-neck and pelvis OARs using deep-learning methods on MRI images. The results show that the proposed method accurately segments the majority of OARs and achieves similar or superior performance compared to state-of-the-art models. The visual evaluation by clinicians also indicates that 81% of the auto-contours are clinically acceptable.
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.

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