3 papers
physics.med-ph2025
MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
Alexander Herold, Daniel Sobotka, Lucian Beer +12
Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis…
cs.CV2025
Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training
Daniel Sobotka, Alexander Herold, Matthias Perkonigg +5
Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodelling, associated with a wide spectrum of diffuse liver d…
eess.IV2025
Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease
Matthias Perkonigg, Nina Bastati, Ahmed Ba-Ssalamah +6
Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here…