Multimodal deep learning to support delineation of neuro-oncology organs based on the EPTN international neurological contouring atlas

We present a multimodal deep learning model for segmenting 25 organs defined in the European Particle Therapy Network (EPTN) international neurological contouring atlas. Multiple input configurations were evaluated on 74 patients using 5-fold cross-validation (59 training, 14-15 per fold for evaluation), with each patient assessed once on unseen data. The dual-input model combining contrast-enhanced T1-weighted magnetic resonance (MR) and computed tomography (CT) achieved the best overall results (median Dice of 0.80, median surface Dice of 0.84), with no added benefit from T2 FLAIR.