@article{77cdf35efed84b78af1aaed127034b82,
title = "Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning",
abstract = "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.",
keywords = "MRI, U-Net, deep learning, head-and-neck, organs-at-risk segmentation, pelvis",
author = "Vanda Czipczer and Bernadett Kolozsv{\'a}ri and Borb{\'a}la De{\'a}k-Karancsi and Capala, {Marta E.} and Pearson, {Rachel A.} and Em{\H o}ke Borz{\'a}si and Zs{\'o}fia Egy{\"u}d and Szilvia Ga{\'a}l and Gy{\"o}ngyi Kelemen and Ren{\'a}ta K{\'o}sz{\'o} and Viktor Paczona and Zolt{\'a}n V{\'e}gv{\'a}ry and Zs{\'o}fia Karancsi and {\'A}d{\'a}m K{\'e}kesi and Edina Czunyi and Irmai, {Blanka H.} and Keresnyei, {N{\'o}ra G.} and Petra Nagyp{\'a}l and Ren{\'a}ta Czab{\'a}ny and Bence Gyalai and Tass, {Bulcs{\'u} P.} and Bal{\'a}zs Cziria and Cristina Cozzini and Lloyd Estkowsky and Lehel Ferenczi and Andr{\'a}s Front{\'o} and Ross Maxwell and Istv{\'a}n Megyeri and Michael Mian and Tao Tan and Jonathan Wyatt and Florian Wiesinger and Katalin Hidegh{\'e}ty and Hazel McCallum and Petit, {Steven F.} and L{\'a}szl{\'o} Rusk{\'o}",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 Czipczer, Kolozsv{\'a}ri, De{\'a}k-Karancsi, Capala, Pearson, Borz{\'a}si, Egy{\"u}d, Ga{\'a}l, Kelemen, K{\'o}sz{\'o}, Paczona, V{\'e}gv{\'a}ry, Karancsi, K{\'e}kesi, Czunyi, Irmai, Keresnyei, Nagyp{\'a}l, Czab{\'a}ny, Gyalai, Tass, Cziria, Cozzini, Estkowsky, Ferenczi, Front{\'o}, Maxwell, Megyeri, Mian, Tan, Wyatt, Wiesinger, Hidegh{\'e}ty, McCallum, Petit and Rusk{\'o}.",
year = "2023",
doi = "10.3389/fphy.2023.1236792",
language = "English",
volume = "11",
journal = "Frontiers in Physics",
issn = "2296-424X",
publisher = "Frontiers Media S.A.",
}