Jan Valošek, Theo Mathieu, Raphaëlle Schlienger, Olivia S. Kowalczyk et Julien Cohen-Adad
Article de revue (2024)
Document en libre accès chez l'éditeur officiel |
Abstract
Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access 3T MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from three datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 ± 0.16 (mean ± standard deviation across testing images and rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation ≤ 1.41%), as well as low inter-session variability (coefficient of variation ≤ 1.30%), indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.
Mots clés
spinal cord; nerve rootlets; magnetic resonance imaging; segmentation; deep learning
Sujet(s): |
1900 Génie biomédical > 1900 Génie biomédical 1900 Génie biomédical > 1901 Technologie biomédicale 1900 Génie biomédical > 1903 Biomécanique |
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Département: | Institut de génie biomédical |
Centre de recherche: | NeuroPoly - Laboratoire de Recherche en Neuroimagerie |
Organismes subventionnaires: | NSERC / CRSNG, Canada Research Chair in Quantitative Magnetic Resonance Imaging, Canadian Institute of Health Research, Canada Foundation for Innovation, Fonds de Recherche du Québec - Santé, Canada First Research Excellence Fund (IVADO and TransMedTech), Courtois NeuroMod Project, Quebec BioImaging Network, INSPIRED (Spinal Research, UK; Wings for Life, Austria; Craig H. Neilsen Foundation, USA), Mila - Tech Transfer Funding Program, Marie Skłodowska-Curie grant, Czech Republic - Ministry of Health |
Numéro de subvention: | CRC-2020-00179, PJT-190258, 32454, 34824, 322736, 324636, RGPIN-2019-07244, 5886, 35450, 101107932, NU22-04-00024 |
URL de PolyPublie: | https://publications.polymtl.ca/58720/ |
Titre de la revue: | Imaging Neuroscience (vol. 2) |
Maison d'édition: | The MIT Press |
DOI: | 10.1162/imag_a_00218 |
URL officielle: | https://doi.org/10.1162/imag_a_00218 |
Dernière modification: | 16 juil. 2024 14:13 |
Citer en APA 7: | Valošek, J., Mathieu, T., Schlienger, R., Kowalczyk, O. S., & Cohen-Adad, J. (2024). Automatic segmentation of the spinal cord nerve rootlets. Imaging Neuroscience, 2. https://doi.org/10.1162/imag_a_00218 |
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