19 citations · 38 across the 6 of their papers we have counts for
6 papers
Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
Mianyong Ding, Maximilian Knoll, Semi Harrabi +7
Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often underperform in pediatric pati…
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Yiling Wang, Elia Lombardo, Adrian Thummerer +25
Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accel…
SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy
Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon +16
Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT),…
Impact of deep learning model uncertainty on manual corrections to auto-segmentation in prostate cancer radiotherapy
Viktor Rogowski, Angelica Svalkvist, Matteo Maspero +12
Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy, yet voxel-wise DL uncertainty maps are rarely presented to clinicians. Purpose: This s…
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…
Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report
Evi M. C. Huijben, Maarten L. Terpstra, Arthur Jr. Galapon +56
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomogra…