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 Kolozsvari , Borbala Deak-Karancsi , Marta E. Capala , Rachel A. Pearson , Emoke Borzasi , Zsofia Egyud , Szilvia Gaal , Gyongyi Kelemen , Renata Koszo , Viktor Paczona , Zoltan Vegvary , Zsofia Karancsi , Adam Kekesi , Edina Czunyi , Blanka H. Irmai , Nora G. Keresnyei , Petra Nagypal , Renata Czabany , Bence Gyalai , Bulcsu P. Tass , Balazs Cziria , Cristina Cozzini , Lloyd Estkowsky , Lehel Ferenczi , Andras Fronto , Ross Maxwell , Istvan Megyeri , Michael Mian , Tao Tan , Jonathan Wyatt , Florian Wiesinger , Katalin Hideghety , Hazel McCallum , Steven F. Petit , Laszlo Rusko Frontiers in physics（2023）
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organs-at-risk segmentation, head-and-neck, pelvis, MRI, deep learning, U-Net
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