138 citations
- Friedrich-Alexander-Universität Erlangen-NürnbergDE7 papers
- Technical University of MunichDE6 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- University of BristolGB5 papers
- Ludwig-Maximilians-Universität MünchenDE4 papers
- Boehringer Ingelheim (Germany)DE3 papers
- Bristol Robotics LaboratoryGB3 papers
- ESPCI ParisFR3 papers
- GulliverFR3 papers
- Max Planck Institute for the Science of LightDE3 papers
- Merck KGaA, Darmstadt (Germany)DE3 papers
- Monash UniversityAU3 papers
Showing eess.IVShow all
3 papers · 1 filter
eess.IV2024★ 11 cited
Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics
Melanie Dohmen, Tuan Truong, Ivo M. Baltruschat +1
Reference metrics have been developed to objectively and quantitatively compare two images. Especially for evaluating the quality of reconstructed or compressed images, these metri…
eess.IV2024★ 66 cited
Similarity and Quality Metrics for MR Image-To-Image Translation
Melanie Dohmen, Mark A. Klemens, Ivo M. Baltruschat +2
Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower n…
eess.IV2023★ 5 cited
A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis
Aline Sindel, Andreas Maier, Vincent Christlein
Unpaired image-to-image translation of retinal images can efficiently increase the training dataset for deep-learning-based multi-modal retinal registration methods. Our method int…