3 citations · 10 across the 6 of their papers we have counts for
6 papers
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik +5
Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models th…
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
Alina F. Dima, Veronika A. Zimmer, Martin J. Menten +8
Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is bei…
A skeletonization algorithm for gradient-based optimization
Martin J. Menten, Johannes C. Paetzold, Veronika A. Zimmer +6
The skeleton of a digital image is a compact representation of its topology, geometry, and scale. It has utility in many computer vision applications, such as image description, se…
Metrics to Quantify Global Consistency in Synthetic Medical Images
Daniel Scholz, Benedikt Wiestler, Daniel Rueckert +1
Image synthesis is increasingly being adopted in medical image processing, for example for data augmentation or inter-modality image translation. In these critical applications, th…
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data
Paul Hager, Martin J. Menten, Daniel Rueckert
Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less dat…
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
Martin J. Menten, Johannes C. Paetzold, Alina Dima +3
Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to…