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Centre de Recherche du CHU Sainte Jus... 1 Department of Anesthesiology Perioper... 1 Department of Neurology Faculty of Me... 1 Department of Neurophysics Max Planck... 1 Department of Neurosurgery Faculty of... 1 Department of Physical Medicine and R... 1 Functional Neuroimaging Unit CRIUGM U... 1 Mila Quebec AI Institute Montreal QC ... 1 NeuroPoly Lab Institute of Biomedical... 1 Spinal Cord Injury Center Balgrist Un... 1
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Centre de Recherche du CHU Sainte Jus... 1 Department of Anesthesiology Perioper... 1 Department of Neurology Faculty of Me... 1 Department of Neurophysics Max Planck... 1 Department of Neurosurgery Faculty of... 1 Department of Physical Medicine and R... 1 Functional Neuroimaging Unit CRIUGM U... 1 Mila Quebec AI Institute Montreal QC ... 1 NeuroPoly Lab Institute of Biomedical... 1 Spinal Cord Injury Center Balgrist Un... 1
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- Karthik, Enamundram Naga
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Valosek, Jan
Autor Valosek, Jan ORCID NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada Mila - Quebec AI Institute, Montreal, QC, Canada Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
- Smith, Andrew C
- Pfyffer, Dario
- Schading-Sassenhausen, Simon
- Farner, Lynn
- Weber, Kenneth A 2nd
- Freund, Patrick
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Cohen-Adad, Julien
Autor Cohen-Adad, Julien ORCID NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada Mila - Quebec AI Institute, Montreal, QC, Canada Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
PubMed
38699309
PubMed Central
PMC11065035
DOI
10.1101/2024.01.03.24300794
PII: 2024.01.03.24300794
Knihovny.cz E-zdroje
PURPOSE: To develop a deep learning tool for the automatic segmentation of T2-weighted intramedullary lesions in spinal cord injury (SCI). MATERIAL AND METHODS: This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The data consisted of T2-weighted MRI acquired using different scanner manufacturers with heterogeneous image resolutions (isotropic/anisotropic), orientations (axial/sagittal), lesion etiologies (traumatic/ischemic/hemorrhagic) and lesions spread across the cervical, thoracic and lumbar spine. The segmentations from the proposed model were visually and quantitatively compared with other open-source baselines. Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual lesion masks and those obtained automatically with SCIseg predictions. RESULTS: MRI data from 191 SCI patients (mean age, 48.1 years ± 17.9 [SD]; 142 males) were used for model training and evaluation. SCIseg achieved the best segmentation performance for both the cord and lesions. There was no statistically significant difference between lesion length and maximal axial damage ratio computed from manually annotated lesions and those obtained using SCIseg. CONCLUSION: Automatic segmentation of intramedullary lesions commonly seen in SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model segments lesions across different etiologies, scanner manufacturers, and heterogeneous image resolutions. SCIseg is open-source and accessible through the Spinal Cord Toolbox.
- Klíčová slova
- Convolution Neural Networks (CNN), MR-Imaging, Segmentation, Spinal Cord, Supervised learning, Trauma,
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