Automated peripheral nerve segmentation for MR-neurography
Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation.
Authors
- Felix Tobias Kurz (ORCID: https://orcid.org/0000-0001-7616-7147)
- Martin Bendszus (ORCID: https://orcid.org/0000-0002-9094-6769)
- Hagen Meredig (ORCID: https://orcid.org/0009-0005-9177-4339)
- Moritz Kronlage (ORCID: https://orcid.org/0000-0001-8648-9350)
- Nedim Christoph Beste (ORCID: https://orcid.org/0000-0001-5420-1199)
- Sabine Heiland (ORCID: https://orcid.org/0000-0002-2796-3451)
- Johann M. E. Jende (ORCID: https://orcid.org/0000-0002-1387-9304)
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1186/s41747-024-00503-8
- Citations
- 8
- Primary Topic
- Peripheral Nerve Disorders
- Type
- article
- Field-Weighted Citation Impact
- 3.23
Funders
- Deutsche Forschungsgemeinschaft
- Else Kröner-Fresenius-Stiftung