29 citations · 40 across the 11 of their papers we have counts for
19 papers · 1 filter
Unsupervised 3D registration through optimization-guided cyclical self-training
Alexander Bigalke, Lasse Hansen, Tony C. W. Mok +1
State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised le…
Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge
Ron Keuth, Mattias Heinrich, Martin Eichenlaub +1
Purpose: Navigation guidance is a key requirement for a multitude of lung interventions using video bronchoscopy. State-of-the-art solutions focus on lung biopsies using electromag…
A denoised Mean Teacher for domain adaptive point cloud registration
Alexander Bigalke, Mattias P. Heinrich
Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of u…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Mattias P. Heinrich, Lasse Hansen
The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg med…
Deep learning based geometric registration for medical images: How accurate can we get without visual features?
Lasse Hansen, Mattias P. Heinrich
As in other areas of medical image analysis, e.g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration. Multi-scale en…