63 citations · 98 across the 8 of their papers we have counts for
8 papers
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…
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…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Daniel Sobotka, Michael Ebner, Ernst Schwartz +8
Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement an…
4D iterative reconstruction of brain fMRI in the moving fetus
Athena Taymourtash, Hamza Kebiri, Sébastien Tourbier +8
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and exc…