ArticlePhysics and imaging in radiation oncology2026
Deep learning segmentation of the cerebral ventricular system and brainstem for pediatric radiotherapy planning.
Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 a deep-learning (DL) model for VS and brainstem segmentation on pediatric magnetic resonance imaging (MRI). Materials and methods: An nnU-Net was trained on MRIs from 76 unique brain tumor cases ( Results: In the GCT-CT cohort, brainstem segmentations achieved median DSCs of >0.85 and HDs Conclusions: Lower performance in the GCT-CT cohort likely reflects noisier CT-based references, interobserver variability, and uncorrected geometric/anatomical mismatch. The model's accuracy supports radiotherapy planning in pediatric brain tumors through risk-structure delineation and target-volume segmentation in GCTs.
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