564-06-007 ISMRM Abstract

MRI contrast translation for full-brain segmentation from T2-weighted contrasts

Primary: Analysis Methods - Image Synthesis and Translation

Secondary: Analysis Methods - Segmentation and Detection

564-06-007 · Segmentation for Neuro Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row E

Keywords: Machine Learning/Artificial Intelligence Freesurfer FastSurfer Brain tissue segmentation Image translation

Accepted

Sebastian Rassmann ¹, David Kügler¹, Martin Reuter^1,2,3

¹German Center for Neurodegenerative Diseases (DZNE e.V.), Bonn, Germany

²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America

³Harvard Medical School, Boston, United States of America

Presenting Author: Sebastian Rassmann

Synopsis

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References

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3. Henschel, L., Kügler, D., & Reuter, M. (2022). FastSurferVINN: Building resolution-independence into deep learning segmentation methods—A solution for HighRes brain MRI. NeuroImage, 251, 118933. https://doi.org/10.1016/j.neuroimage.2022.118933 [doi]

4. Rassmann, S., Kügler, D., Ewert, C., & Reuter, M. (2025). Regression is all you need for medical image translation. arXiv preprint arXiv:2505.02048. https://doi.org/10.48550/arXiv.2505.02048 [doi]

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10. Billot, B., Greve, D. N., Puonti, O., et al. (2023). SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Medical image analysis, 86, 102789. https://doi.org/10.1016/j.media.2023.102789 [doi]

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http://echo.ismrm.org/p/ISMRM2026/564-06-007