116 citations · 117 across the 3 of their papers we have counts for
3 papers
Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
Mianyong Ding, Matteo Maspero, Annemieke S Littooij +5
Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and…
Deep learning-based brain segmentation model performance validation with clinical radiotherapy CT
Selena Huisman, Matteo Maspero, Marielle Philippens +2
Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, i…
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon +6
Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomo…