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

Vanda Czipczer, Bernadett Kolozsvári, Borbála Deák-Karancsi, Marta E. Capala, Rachel A. Pearson, Emőke Borzási, Zsófia Együd, Szilvia Gaál, Gyöngyi Kelemen, Renáta Kószó, Viktor Paczona, Zoltán Végváry, Zsófia Karancsi, Ádám Kékesi, Edina Czunyi, Blanka H. Irmai, Nóra G. Keresnyei, Petra Nagypál, Renáta Czabány, Bence GyalaiBulcsú P. Tass, Balázs Cziria, Cristina Cozzini, Lloyd Estkowsky, Lehel Ferenczi, András Frontó, Ross Maxwell, István Megyeri, Michael Mian, Tao Tan, Jonathan Wyatt, Florian Wiesinger, Katalin Hideghéty, Hazel McCallum, Steven F. Petit, László Ruskó

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摘要

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.

原文English
文章編號1236792
期刊Frontiers in Physics
11
DOIs
出版狀態Published - 2023
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