Design and evaluation of a deep learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net.

L Melerowitz¹, S Sreenivasa¹, M Nachbar¹, A Stsefanenka¹, M Beck¹, C Senger¹, N Predescu², S Ullah Akram², V Budach¹, D Zips¹, M Heiland³, S Nahles³, C Stromberger¹

¹Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany.

²MVision AI, Paciuksenkatu 29 00270 Helsinki, Finland.

³Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Augustenburger Platz 1, 13353, Berlin, Germany.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Current segmentation approaches for radiation treatment planning in head and neck cancer patients (HNCP) typically consider the entire mandible as an organ at risk, whereas segmentation of the maxilla remains uncommon. Accurate risk assessment for osteoradionecrosis (ORN) or implant-based dental rehabilitation after radiation therapy may require a nuanced analysis of dose distribution…

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