150 citations
- Universitätsmedizin GöttingenDE33 papers
- University of GöttingenDE26 papers
- Graz University of TechnologyAT12 papers
- BioTechMed-GrazAT7 papers
- Nanoscale Microscopy and Molecular Physiology of the Brain Cluster of Excellence 171 — DFG Research Center 103DE7 papers
- Heidelberg UniversityDE6 papers
- University Hospital HeidelbergDE6 papers
- Charité - Universitätsmedizin BerlinDE5 papers
- Max Planck Institute for Dynamics and Self-OrganizationDE5 papers
- Ludwig-Maximilians-Universität MünchenDE4 papers
- Max Planck Institute for Biophysical ChemistryDE4 papers
- Advanced Imaging Research (United States)US3 papers
7 papers · 1 filter
Investigating the heterogeneity of "study twins"
Christian Röver, Tim Friede
Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favour of a common-effect model. One such case may be given by t…
Assessment of Deep Learning Segmentation for Real-Time Free-Breathing Cardiac Magnetic Resonance Imaging at Rest and Under Exercise Stress
Martin Schilling, Christina Unterberg-Buchwald, Joachim Lotz +1
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under…
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…
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation
Guanxiong Luo, Xiaoqing Wang, Mortiz Blumenthal +5
Purpose: In this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images. The priors can then be used for regularization in r…
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
Georgii Kostiuchik, Lalith Sharan, Benedikt Mayer +3
Purpose: Machine learning models can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes of inter…
Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart
Yunxiao Zhang, Moritz Kalhöfer-Köchling, Eberhard Bodenschatz +1
Left ventricular (LV) stiffness and contractility, characterized by the end-diastolic and end-systolic pressure-volume relationships (EDPVR & ESPVR), are two important indicators o…