5 papers
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
Viktor Rogowski, Maarten L. Terpstra, Niklas Wahl +30
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acqui…
Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net
Ricardo Coimbra Brioso, Lorenzo Mondo, Damiano Dei +4
Accurate delineation of the Clinical Target Volume (CTV) is essential for radiotherapy planning, yet remains time-consuming and difficult to assess, especially for complex treatmen…
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
Ricardo Coimbra Brioso, Giulio Sichili, Damiano Dei +4
Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medical image segmentation due to…
ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy
Ricardo Coimbra Brioso, Leonardo Crespi, Andrea Seghetto +5
A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the delivery. To this end, Cone Beam…
Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors
Ricardo Coimbra Brioso, Damiano Dei, Nicola Lambri +3
Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learnin…