Segmentation of spinal rootlets across MRI contrasts with RootletSeg
Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-level analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T turbo spin echo T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years ± 6.53 [SD]; 28 [56%] males, 22 [44%] females) and achieved a mean ± SD Dice score of 0.67 ± 0.09 for T1w-INV2, 0.65 ± 0.11 for UNIT1, 0.64 ± 0.08 for T2w, and 0.62 ± 0.10 for T1w-INV1 contrasts. RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses.
Authors
- Virginie Callot (ORCID: https://orcid.org/0000-0003-0850-1742)
- Sandrine Bédard (ORCID: https://orcid.org/0000-0001-9859-1133)
- Falk Eippert (ORCID: https://orcid.org/0000-0002-3986-1719)
- Ulrike Horn (ORCID: https://orcid.org/0000-0001-9119-0468)
- Jiří Chmelík (ORCID: https://orcid.org/0000-0001-9950-6279)
- Julien Cohen-Adad
- Katerina Krejci
- Jan Valošek
Institutions
- Centre National de la Recherche Scientifique (FR)
- Centre Hospitalier Universitaire Sainte-Justine (CA)
- Institut d'Imagerie Biomédicale (FR)
- Universitätsklinik Balgrist (CH)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Centre de Résonance Magnétique Biologique et Médicale (FR)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Polytechnique Montréal (CA)
- Brno University of Technology (CZ)
- Palacký University Olomouc (CZ)
- Université de Montréal (CA)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-05-02
- DOI
- https://doi.org/10.1038/s41598-026-49164-0
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Canada Foundation for Innovation
- Ministerstvo Zdravotnictví Ceské Republiky
- Canada First Research Excellence Fund
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé