{"ID":2870034,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16255","arxiv_id":"2509.16255","title":"Segmentation of spinal rootlets across MRI contrasts with RootletSeg","abstract":"Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Methods: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years $\\pm$ 6.53 [SD]; 28 [56%] males, 22 [44%] females) achieved a mean $\\pm$ SD Dice score of 0.67 $\\pm$ 0.09 for T1w-INV2, 0.65 $\\pm$ 0.11 for UNIT1, 0.64 $\\pm$ 0.08 for T2w, and 0.62 $\\pm$ 0.10 for T1w-INV1 contrasts. Spinal-vertebral level correspondence showed a progressively increasing rostrocaudal shift, with Bland-Altman bias ranging from 0.00 to 8.15 mm (median difference between level midpoints). Conclusion: 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, including lesion classification, neuromodulation therapy, and functional MRI group analysis.","short_abstract":"Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Methods: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] I...","url_abs":"https://arxiv.org/abs/2509.16255","url_pdf":"https://arxiv.org/pdf/2509.16255v2","authors":"[\"Katerina Krejci\",\"Jiri Chmelik\",\"Sandrine Bedard\",\"Falk Eippert\",\"Ulrike Horn\",\"Virginie Callot\",\"Julien Cohen-Adad\",\"Jan Valosek\"]","published":"2025-09-17T19:44:50Z","proceeding":"q-bio.TO","tasks":"[\"q-bio.TO\",\"eess.IV\",\"physics.med-ph\"]","methods":"[]","has_code":false}