MRI annotation using an inversion-based preprocessing for CT model adaptation
CT segmentation models can create presegmentations for many structures in MRI scans. T1w MRI scans require an additional inversion step before segmenting with a CT model. Results were consistent for a large multiclass model (i.e., TotalSegmentator) and a smaller model for renal cell carcinoma.
Authors
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Jawed Nawabi (ORCID: https://orcid.org/0000-0002-1137-0643)
- Maximilian Rattunde
- Alessa Hering (ORCID: https://orcid.org/0000-0002-7602-803X)
- Leonhard Donle (ORCID: https://orcid.org/0000-0002-7931-4621)
- Hartmut Häntze (ORCID: https://orcid.org/0000-0002-6204-571X)
- Felix J. Dorfner (ORCID: https://orcid.org/0009-0003-5914-3233)
- Lina Xu (ORCID: https://orcid.org/0009-0007-4119-1033)
Institutions
- Harvard University (US)
- Radboud University Nijmegen (NL)
- TUM Klinikum (DE)
- University Medical Center (US)
- Radboud University Medical Center (NL)
- Humboldt-Universität zu Berlin (DE)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Deutsches Herzzentrum München (DE)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-09-19
- DOI
- https://doi.org/10.1186/s41747-025-00626-6
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Wilhelm Sander-Stiftung
- European Commission