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Nejvíce citované: 21232610

4 citací v PubMed Filtry

Nejvíce citovaný článek - PubMed ID 21232610

Záznam nenalezen v Medvik. Navštivte PubMed 21232610

Článek

SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans

Naga Karthik, Enamundram
Autor Naga Karthik, Enamundram ORCID From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Valošek, Jan
Autor Valošek, Jan ORCID From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Smith, Andrew C
Autor Smith, Andrew C ORCID From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Pfyffer, Dario
Autor Pfyffer, Dario ORCID From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Schading-Sassenhausen, Simon
Autor Schading-Sassenhausen, Simon From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Farner, Lynn
Autor Farner, Lynn ORCID From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Weber, Kenneth A 2nd
Autor Weber, Kenneth A ORCID From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Freund, Patrick
Autor Freund, Patrick From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)
Cohen-Adad, Julien
Autor Cohen-Adad, Julien From the NeuroPoly Laboratory, Institute of Biomedical Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute, Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada (J.C.A.)

Radiology. Artificial intelligence. 2025 Jan ; 7 (1) : e240005.

Radiol Artif Intell
ISSN 2638-6100
Zdroj

Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023. The data consisted of T2-weighted MRI scans acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic, and lumbar spine. A deep learning model, SCIseg (which is open source and accessible through the Spinal Cord Toolbox, version 6.2 and above), was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg, and contrast-agnostic, all part of the Spinal Cord Toolbox). The Wilcoxon signed rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results The study included 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 [74%] male patients). SCIseg achieved a mean Dice score of 0.92 ± 0.07 and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and automatically extracted clinically relevant lesion characteristics. Keywords: Spinal Cord, Trauma, Segmentation, MR Imaging, Supervised Learning, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license.

Klíčová slova
Convolutional Neural Network (CNN), MR Imaging, Segmentation, Spinal Cord, Supervised Learning, Trauma,
MeSH
deep learning * MeSH
dospělí MeSH
interpretace obrazu počítačem metody MeSH
lidé středního věku MeSH
lidé MeSH
magnetická rezonanční tomografie * metody MeSH
poranění míchy * diagnostické zobrazování patologie MeSH
retrospektivní studie MeSH
senioři MeSH
Check Tag
dospělí MeSH
lidé středního věku MeSH
lidé MeSH
mužské pohlaví MeSH
senioři MeSH
ženské pohlaví MeSH
Publikační typ
časopisecké články MeSH

Článek

SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury

medRxiv. 2024 Apr 21 ; () : . [epub] 20240421

medRxiv
Zdroj

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,
Publikační typ
časopisecké články MeSH
preprinty MeSH

Článek

Generic acquisition protocol for quantitative MRI of the spinal cord

Nature protocols. 2021 Oct ; 16 (10) : 4611-4632. [epub] 20210816

Nat Protoc
ISSN 1750-2799
Zdroj

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

MeSH
dospělí MeSH
lidé MeSH
magnetická rezonanční tomografie * metody MeSH
mícha * diagnostické zobrazování MeSH
počítačové zpracování obrazu metody MeSH
Check Tag
dospělí MeSH
lidé MeSH
mužské pohlaví MeSH
ženské pohlaví MeSH
Publikační typ
časopisecké články MeSH

Článek

HARDI-ZOOMit protocol improves specificity to microstructural changes in presymptomatic myelopathy

Scientific reports. 2020 Oct 16 ; 10 (1) : 17529. [epub] 20201016

Sci Rep
ISSN 2045-2322
Zdroj

Diffusion magnetic resonance imaging (dMRI) proved promising in patients with non-myelopathic degenerative cervical cord compression (NMDCCC), i.e., without clinically manifested myelopathy. Aim of the study is to present a fast multi-shell HARDI-ZOOMit dMRI protocol and validate its usability to detect microstructural myelopathy in NMDCCC patients. In 7 young healthy volunteers, 13 age-comparable healthy controls, 18 patients with mild NMDCCC and 15 patients with severe NMDCCC, the protocol provided higher signal-to-noise ratio, enhanced visualization of white/gray matter structures in microstructural maps, improved dMRI metric reproducibility, preserved sensitivity (SE = 87.88%) and increased specificity (SP = 92.31%) of control-patient group differences when compared to DTI-RESOLVE protocol (SE = 87.88%, SP = 76.92%). Of the 56 tested microstructural parameters, HARDI-ZOOMit yielded significant patient-control differences in 19 parameters, whereas in DTI-RESOLVE data, differences were observed in 10 parameters, with mostly lower robustness. Novel marker the white-gray matter diffusivity gradient demonstrated the highest separation. HARDI-ZOOMit protocol detected larger number of crossing fibers (5-15% of voxels) with physiologically plausible orientations than DTI-RESOLVE protocol (0-8% of voxels). Crossings were detected in areas of dorsal horns and anterior white commissure. HARDI-ZOOMit protocol proved to be a sensitive and practical tool for clinical quantitative spinal cord imaging.

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