4 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…
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…
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),…