58 citations
- Technical University of MunichDE14 papers
- Imperial College LondonGB8 papers
- Helmholtz MunichDE3 papers
- Ludwig-Maximilians-Universität MünchenDE2 papers
- The University of Texas MD Anderson Cancer CenterUS2 papers
- University of EdinburghGB2 papers
- Aarhus UniversityDK1 paper
- Aarhus University HospitalDK1 paper
- Cancer Research UK Cambridge CenterGB1 paper
- Christian-Albrechts-Universität zu KielDE1 paper
- German Cancer Research CenterDE1 paper
- German Centre for Cardiovascular ResearchDE1 paper
5 papers · 1 filter
Attention-aware non-rigid image registration for accelerated MR imaging
Aya Ghoul, Jiazhen Pan, Andreas Lingg +6
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic imag…
Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
Robert Graf, Joachim Schmitt, Sarah Schlaeger +8
Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents ch…
Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance
Chen Qin, Daniel Rueckert
Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease di…
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
Turkay Kart, Wenjia Bai, Ben Glocker +1
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due…
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
Hans Liebl, David Schinz, Anjany Sekuboyina +13
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning…