Multimodal deep learning to support delineation of neuro-oncology organs based on the EPTN international neurological contouring atlas.
Authors
Barragán-Montero AM,Huet-Dastarac M,Di Perri D,Hofstede D,Birimac NE,Geerts M,Roberfroid B,Quéré E,Lee JA,Roelofs E,van Elmpt W,Eekers DBP,Zegers CML
Affiliations (4)
Affiliations (4)
- UCLouvain - Institut de Recherche Expérimentale et Clinique - Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium.
- Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.
- École nationale supérieure de techniques avancées Bretagne (ENSTA), Brest, France.
Abstract
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.
Topics
Journal Article