Peer-Reviewed Publication
Phys Imaging Radiat Oncol2026;41101086.September 1, 2026Journal Article
Deep learning segmentation of the cerebral ventricular system and brainstem for pediatric radiotherapy planning.
Feline Heinzelmann1,2,3, Fabian Hörst4,5, Lukas Heine4,5, Jana Fragemann4,5, Moritz Rempe2,4,5, Gijs Luijten4,6,5, Sarah Peters1,3,7, Julian Hlouschek1,3,7, Jan Egger4,5, Beate Timmermann1,3,7,8,5, Jens Kleesiek2,3,4,8,5, Christian Bäumer1,2,3,8
1West German Proton Therapy Centre Essen (WPE), Essen, Germany.
2TU Dortmund University, Department of Physics, Dortmund, Germany.
3West German Cancer Center (WTZ), University Hospital Essen, Essen, Germany.
4Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany.
5University of Duisburg-Essen, Faculty of Medicine, Essen, Germany.
6Center for Virtual and Extended Reality in Medicine (ZvRM), University Hospital Essen (AöR), Essen, Germany.
7Clinic for Particle Therapy, University Hospital Essen, Essen, Germany.
8German Cancer Consortium (DKTK), Essen, Germany.
Abstract
BACKGROUND AND PURPOSE: Segmentation of the cerebral ventricular system (VS) is central to radiotherapy, both for whole-ventricular irradiation in intracranial germ cell tumors (GCTs) and protection of the periventricular region. Complex, variable VS anatomy makes manual delineation challenging and hampers auto-segmentation model development. We reported the evaluation and workflow integration of…
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