11 citations · 32 across the 6 of their papers we have counts for
6 papers
PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches
Henrik Skibbe, Michal Byra, Akiya Watakabe +2
We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration. Unlike other methods, our method uses compact patches of…
Deep Neural Patchworks: Coping with Large Segmentation Tasks
Marco Reisert, Maximilian Russe, Samer Elsheikh +2
Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in parti…
Optimization and Validation of Diffusion MRI-based Fiber Tracking with Neural Tracer Data as a Reference
Carlos Enrique Gutierrez, Henrik Skibbe, Ken Nakae +11
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network…
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
Marco Reisert, Volker A. Coenen, Christoph Kaller +2
In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract…
Probabilistic tractography, Path Integrals and the Fokker Planck equation
Marco Reisert
Probabilistic tractography based on diffusion weighted MRI has become a powerful approach for quantifying structural brain connectivities. In several works the similarity of probab…
Left-Invariant Diffusion on the Motion Group in terms of the Irreducible Representations of SO(3)
Marco Reisert, Henrik Skibbe
In this work we study the formulation of convection/diffusion equations on the 3D motion group SE(3) in terms of the irreducible representations of SO(3). Therefore, the left-invar…