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
Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery
Jeanny Pan, Philipp Seeböck, Christoph Fürböck +5
Identifying new disease-related patterns in medical imaging data with the help of machine learning enlarges the vocabulary of recognizable findings. This supports diagnostic and pr…
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…