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Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Wollborn J, Lang G, Hassel F. Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning. Bioengineering (Basel) 2023; 10:1072. [PMID: 37760174 PMCID: PMC10525778 DOI: 10.3390/bioengineering10091072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/21/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
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Deep Learning-Based Auto-Segmentation of Spinal Cord Internal Structure of Diffusion Tensor Imaging in Cervical Spondylotic Myelopathy. Diagnostics (Basel) 2023; 13:diagnostics13050817. [PMID: 36899962 PMCID: PMC10000612 DOI: 10.3390/diagnostics13050817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/14/2023] [Accepted: 02/19/2023] [Indexed: 02/24/2023] Open
Abstract
Cervical spondylotic myelopathy (CSM) is a chronic disorder of the spinal cord. ROI-based features on diffusion tensor imaging (DTI) provide additional information about spinal cord status, which would benefit the diagnosis and prognosis of CSM. However, the manual extraction of the DTI-related features on multiple ROIs is time-consuming and laborious. In total, 1159 slices at cervical levels from 89 CSM patients were analyzed, and corresponding fractional anisotropy (FA) maps were calculated. Eight ROIs were drawn, covering both sides of lateral, dorsal, ventral, and gray matter. The UNet model was trained with the proposed heatmap distance loss for auto-segmentation. Mean Dice coefficients on the test dataset for dorsal, lateral, and ventral column and gray matter were 0.69, 0.67, 0.57, 0.54 on the left side and 0.68, 0.67, 0.59, 0.55 on the right side. The ROI-based mean FA value based on segmentation model strongly correlated with the value based on manual drawing. The percentages of the mean absolute error between the two values of multiple ROIs were 0.07, 0.07, 0.11, and 0.08 on the left side and 0.07, 0.1, 0.1, 0.11, and 0.07 on the right side. The proposed segmentation model has the potential to offer a more detailed spinal cord segmentation and would be beneficial for quantifying a more detailed status of the cervical spinal cord.
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Toufani H, Vard A, Adibi I. A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients. Phys Med 2021; 89:51-62. [PMID: 34352676 DOI: 10.1016/j.ejmp.2021.07.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/28/2021] [Accepted: 07/21/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper presents a fully-automated image processing pipeline which quantifies the SC volume of MRI images. METHODS In the proposed pipeline, after conducting some pre-processing tasks, a deep convolutional network is utilized to segment the spinal cord cross-sectional area (SCCSA) of each slice. After full segmentation, certain extra slices interpolate between each two adjacent slices using the shape-based interpolation method. Then, a 3D model of the SC is reconstructed, and, by counting the voxels of it, the SC volume is calculated. The performance of the proposed method for the SCCSA segmentation is evaluated on 140 MRI images. Subsequently, to demonstrate the application of the proposed pipeline, we study the differentiations of SC atrophy between 38 Multiple Sclerosis (MS) and 25 Neuromyelitis Optica Spectrum Disorder (NMOSD) patients. RESULTS The experimental results of the SCCSA segmentation indicate that the proposed method, adapted by Mask R-CNN, presented the most satisfactory result with the average Dice coefficient of 0.96. For this method, statistical metrics including sensitivity, specificity, accuracy, and precision are 97.51%, 99.98%, 99.92%, and 98.04% respectively. Moreover, the t-test result (p-value = 0.00089) verified a significant difference between the SC atrophy of MS and NMOSD patients. CONCLUSION The pipeline efficiently quantifies the SC volume of MRI images and can be utilized as an affordable computer-aided tool for diagnostic purposes.
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Affiliation(s)
- Hediyeh Toufani
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Vard
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Iman Adibi
- Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Dostál M, Keřkovský M, Staffa E, Bednařík J, Šprláková-Puková A, Mechl M. Voxelwise analysis of diffusion MRI of cervical spinal cord using tract-based spatial statistics. Magn Reson Imaging 2020; 73:23-30. [PMID: 32688050 DOI: 10.1016/j.mri.2020.07.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 07/14/2020] [Indexed: 11/24/2022]
Abstract
Robust voxelwise analysis using tract-based spatial statistics (TBSS) together with permutation statistical method is standardly used in analyzing diffusion tensor imaging (DTI) of brain. A similar analytical method could be useful when studying DTI of cervical spinal cord. Based on anatomical data of sixty-four healthy volunteers, white (WM) and gray matter (GM) masks were created and subsequently registered into DTI space. Using TBSS, two skeleton types were created (single line and dilated for WM as well as GM). From anatomical data, percentage rates of overlap were calculated for all skeletons in relation to WM and GM masks. Voxelwise analysis of fractional anisotropy values depending on age and sex was conducted. Correlation of fraction anisotropy values with age of subjects was also evaluated. The two WM skeleton types showed a high overlap rate with WM masks (~94%); GM skeletons showed lower rates (56% and 42%, respectively, for single line and dilated). WM and GM areas where fraction anisotropy values differ between sexes were identified (p < .05). Furthermore, using voxelwise analysis such WM voxels were identified where fraction anisotropy values differ depending on age (p < .05) and in these voxels linear dependence of fraction anisotropy and age (r = -0.57, p < .001) was confirmed by regression analysis. This dependence was not proven when using WM anatomical masks (r = -0.21, p = .10). The analytical approach presented shown to be useful for group analysis of DTI data for cervical spinal cord.
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Affiliation(s)
- Marek Dostál
- Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic; Faculty of Medicine, Department of Biophysics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic.
| | - Erik Staffa
- Faculty of Medicine, Department of Biophysics, Masaryk University, Brno, Czech Republic
| | - Josef Bednařík
- Department of Neurology, University Hospital Brno and Masaryk University, Czech Republic
| | - Andrea Šprláková-Puková
- Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic
| | - Marek Mechl
- Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic
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Faillot M, Morar S, Delphine S, El-Mendili M, Ducreux D, Parker F, Aghakhani N. Prospective Follow-up of Intramedullary Slitlike Cavities: A Consecutive Series of 48 Patients. Front Neurol 2020; 11:495. [PMID: 32595590 PMCID: PMC7304370 DOI: 10.3389/fneur.2020.00495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 05/06/2020] [Indexed: 11/13/2022] Open
Abstract
Object: Predicting whether intramedullary slitlike cavity (SC) will worsen over time or remain stable is an outstanding clinical challenge. The aim of this study was to identify early features of SC (clinical and magnetic resonance imaging [MRI] findings). Methods: We prospectively included all patients referred to our institution following the discovery of a SC and divided them in two groups: typical SC (defined as a cavity spanning fewer than three vertebrae, not enlarging the spinal cord, and located at the midline between the anterior third and posterior two-thirds of the spinal cord) or atypical SC (all others). Clinical evolution and changes in MRI features were evaluated during follow-up. In some patients, diffusion tensor imaging was performed and cervical cord cross-sectional area was analyzed. Results: A total of 48 consecutive patients were included in the study. The mean follow-up was 58 months. Of the seven patients presenting with deficits at first consultation, two worsened and five remained stable. Of the 41 patients without deficits, seven worsened and 34 remained stable. None of the patients developed severe motor deficits or experienced enlargement of the cavity; 7% of patients who presented with typical SC worsened compared with 35% with atypical SC. The negative predictive value was 0.93 (P = 0.02). Conclusion: Most patients remained stable and a subset of patients developed minor motor deficits. For clinical management, we propose surveillance of patients with a typical SC and close follow-up of those with an atypical SC and/or presenting with deficits.
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Affiliation(s)
- Matthieu Faillot
- Neurosurgery Department, Bicêtre University Hospital, Assistance Publique Hôpitaux de Paris (APHP), Université Paris-Sud, Le Kremlin-Bicêtre, France
- CRMR C-MAVEM (Centre de reference Maladies Rares Chiari-Syringomyélie et Malformations vertébrales et médullaires rares/Reference Center for Rare Diseases: Chiari-Syringomyelia and Rare Malformations of the Spine and Spinal Cord), Paris, France
| | - Silvia Morar
- Neurosurgery Department, Bicêtre University Hospital, Assistance Publique Hôpitaux de Paris (APHP), Université Paris-Sud, Le Kremlin-Bicêtre, France
- CRMR C-MAVEM (Centre de reference Maladies Rares Chiari-Syringomyélie et Malformations vertébrales et médullaires rares/Reference Center for Rare Diseases: Chiari-Syringomyelia and Rare Malformations of the Spine and Spinal Cord), Paris, France
| | - Sebastien Delphine
- Biomedical imaging laboratory (LIB, Laboratoire d'Imagerie Biomédicale), CNRS, UMR 7371, INSERM, UMR-S 1146, Sorbonne Université, Paris, France
| | - Mounir El-Mendili
- Neurology Department, Icahn School of Medicine at Mount Sinaï, NewYork, NY, United States
| | - Denis Ducreux
- Neuroradiology Department, Bicêtre University Hospital, Assistance Publique Hôpitaux de Paris (APHP), Université Paris-Sud, Le Kremlin-Bicêtre, France
| | - Fabrice Parker
- Neurosurgery Department, Bicêtre University Hospital, Assistance Publique Hôpitaux de Paris (APHP), Université Paris-Sud, Le Kremlin-Bicêtre, France
- CRMR C-MAVEM (Centre de reference Maladies Rares Chiari-Syringomyélie et Malformations vertébrales et médullaires rares/Reference Center for Rare Diseases: Chiari-Syringomyelia and Rare Malformations of the Spine and Spinal Cord), Paris, France
| | - Nozar Aghakhani
- Neurosurgery Department, Bicêtre University Hospital, Assistance Publique Hôpitaux de Paris (APHP), Université Paris-Sud, Le Kremlin-Bicêtre, France
- CRMR C-MAVEM (Centre de reference Maladies Rares Chiari-Syringomyélie et Malformations vertébrales et médullaires rares/Reference Center for Rare Diseases: Chiari-Syringomyelia and Rare Malformations of the Spine and Spinal Cord), Paris, France
- *Correspondence: Nozar Aghakhani
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Sabaghian S, Dehghani H, Batouli SAH, Khatibi A, Oghabian MA. Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm. Spinal Cord 2020; 58:811-820. [PMID: 32132652 DOI: 10.1038/s41393-020-0429-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 01/26/2020] [Accepted: 01/27/2020] [Indexed: 11/09/2022]
Abstract
STUDY DESIGN Method development. OBJECTIVES To develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images. SETTING University-based laboratory, Tehran, Iran. METHODS T2 structural volumes acquired from the spinal cord of 20 uninjured volunteers on a 3T MR scanner. We proposed an automatic method for spinal cord segmentation based on the K-means clustering algorithm in 3D images and compare our results with two available segmentation methods (PropSeg, DeepSeg) implemented in the Spinal Cord Toolbox. Dice and Hausdorff were used to compare the results of our method (K-Seg) with the manual segmentation, PropSeg, and DeepSeg. RESULTS The accuracy of our automatic segmentation method for T2-weighted images was significantly better or similar to the SCT methods, in terms of 3D DC (p < 0.001). The 3D DCs were respectively (0.81 ± 0.04) and Hausdorff Distance (12.3 ± 2.48) by the K-Seg method in contrary to other SCT methods for T2-weighted images. CONCLUSIONS The output with similar protocols showed that K-Seg results match the manual segmentation better than the other methods especially on the thoracolumbar levels in the spinal cord due to the low image contrast as a result of poor SNR in these areas.
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Affiliation(s)
- Sahar Sabaghian
- Department of Software, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.,Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Dehghani
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran.,Department of Medical Physics and Biomedical Engineering, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran.,Department of Neuroscience and Addiction studies, School of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK.,Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Mohammad Ali Oghabian
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran. .,Department of Medical Physics and Biomedical Engineering, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran.
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El Mendili MM, Querin G, Bede P, Pradat PF. Spinal Cord Imaging in Amyotrophic Lateral Sclerosis: Historical Concepts-Novel Techniques. Front Neurol 2019; 10:350. [PMID: 31031688 PMCID: PMC6474186 DOI: 10.3389/fneur.2019.00350] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 03/21/2019] [Indexed: 01/13/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is the most common adult onset motor neuron disease with no effective disease modifying therapies at present. Spinal cord degeneration is a hallmark feature of ALS, highlighted in the earliest descriptions of the disease by Lockhart Clarke and Jean-Martin Charcot. The anterior horns and corticospinal tracts are invariably affected in ALS, but up to recently it has been notoriously challenging to detect and characterize spinal pathology in vivo. With recent technological advances, spinal imaging now offers unique opportunities to appraise lower motor neuron degeneration, sensory involvement, metabolic alterations, and interneuron pathology in ALS. Quantitative spinal imaging in ALS has now been used in cross-sectional and longitudinal study designs, applied to presymptomatic mutation carriers, and utilized in machine learning applications. Despite its enormous clinical and academic potential, a number of physiological, technological, and methodological challenges limit the routine use of computational spinal imaging in ALS. In this review, we provide a comprehensive overview of emerging spinal cord imaging methods and discuss their advantages, drawbacks, and biomarker potential in clinical applications, clinical trial settings, monitoring, and prognostic roles.
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Affiliation(s)
- Mohamed Mounir El Mendili
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France
| | - Giorgia Querin
- Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France
| | - Peter Bede
- Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France.,Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Pierre-François Pradat
- Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France
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Chipika RH, Finegan E, Li Hi Shing S, Hardiman O, Bede P. Tracking a Fast-Moving Disease: Longitudinal Markers, Monitoring, and Clinical Trial Endpoints in ALS. Front Neurol 2019; 10:229. [PMID: 30941088 PMCID: PMC6433752 DOI: 10.3389/fneur.2019.00229] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 02/22/2019] [Indexed: 12/13/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) encompasses a heterogeneous group of phenotypes with different progression rates, varying degree of extra-motor involvement and divergent progression patterns. The natural history of ALS is increasingly evaluated by large, multi-time point longitudinal studies, many of which now incorporate presymptomatic and post-mortem assessments. These studies not only have the potential to characterize patterns of anatomical propagation, molecular mechanisms of disease spread, but also to identify pragmatic monitoring markers. Sensitive markers of progressive neurodegenerative change are indispensable for clinical trials and individualized patient care. Biofluid markers, neuroimaging indices, electrophysiological markers, rating scales, questionnaires, and other disease-specific instruments have divergent sensitivity profiles. The discussion of candidate monitoring markers in ALS has a dual academic and clinical relevance, and is particularly timely given the increasing number of pharmacological trials. The objective of this paper is to provide a comprehensive and critical review of longitudinal studies in ALS, focusing on the sensitivity profile of established and emerging monitoring markers.
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Affiliation(s)
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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Fouladivanda M, Kazemi K, Helfroush MS, Shakibafard A. Morphological active contour driven by local and global intensity fitting for spinal cord segmentation from MR images. J Neurosci Methods 2018; 308:116-128. [DOI: 10.1016/j.jneumeth.2018.07.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 07/18/2018] [Accepted: 07/18/2018] [Indexed: 10/28/2022]
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Dostál M, Keřkovský M, Korit′áková E, Němcová E, Stulík J, Staňková M, Bernard V. Analysis of diffusion tensor measurements of the human cervical spinal cord based on semiautomatic segmentation of the white and gray matter. J Magn Reson Imaging 2018; 48:1217-1227. [DOI: 10.1002/jmri.26166] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/10/2018] [Indexed: 02/06/2023] Open
Affiliation(s)
- Marek Dostál
- Department of Biophysics, Faculty of Medicine; Masaryk University; Brno Czech Republic
- Department of Radiology; University Hospital Brno and Masaryk University; Brno Czech Republic
| | - Miloš Keřkovský
- Department of Radiology; University Hospital Brno and Masaryk University; Brno Czech Republic
| | - Eva Korit′áková
- Institute of Biostatistics and Analyses, Faculty of Medicine; Masaryk University; Brno Czech Republic
| | - Eva Němcová
- Department of Radiology; University Hospital Brno and Masaryk University; Brno Czech Republic
| | - Jakub Stulík
- Department of Radiology; University Hospital Brno and Masaryk University; Brno Czech Republic
| | - Monika Staňková
- Department of Radiology; University Hospital Brno and Masaryk University; Brno Czech Republic
| | - Vladan Bernard
- Department of Biophysics, Faculty of Medicine; Masaryk University; Brno Czech Republic
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PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. Neuroimage 2018; 165:170-179. [DOI: 10.1016/j.neuroimage.2017.10.041] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 09/16/2017] [Accepted: 10/20/2017] [Indexed: 11/17/2022] Open
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12
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Atlas-Free Cervical Spinal Cord Segmentation on Midsagittal T2-Weighted Magnetic Resonance Images. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:8691505. [PMID: 29065658 PMCID: PMC5435982 DOI: 10.1155/2017/8691505] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 02/14/2017] [Accepted: 02/15/2017] [Indexed: 11/29/2022]
Abstract
An automatic atlas-free method for segmenting the cervical spinal cord on midsagittal T2-weighted magnetic resonance images (MRI) is presented. Pertinent anatomical knowledge is transformed into constraints employed at different stages of the algorithm. After picking up the midsagittal image, the spinal cord is detected using expectation maximization and dynamic programming (DP). Using DP, the anterior and posterior edges of the spinal canal and the vertebral column are detected. The vertebral bodies and the intervertebral disks are then segmented using region growing. Then, the anterior and posterior edges of the spinal cord are detected using median filtering followed by DP. We applied this method to 79 noncontrast MRI studies over a 3-month period. The spinal cords were detected in all cases, and the vertebral bodies were successfully labeled in 67 (85%) of them. Our algorithm had very good performance. Compared to manual segmentation results, the Jaccard indices ranged from 0.937 to 1, with a mean of 0.980 ± 0.014. The Hausdorff distances between the automatically detected and manually delineated anterior and posterior spinal cord edges were both 1.0 ± 0.5 mm. Used alone or in combination, our method lays a foundation for computer-aided diagnosis of spinal diseases, particularly cervical spondylotic myelopathy.
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13
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Bede P, Iyer PM, Finegan E, Omer T, Hardiman O. Virtual brain biopsies in amyotrophic lateral sclerosis: Diagnostic classification based on in vivo pathological patterns. NEUROIMAGE-CLINICAL 2017; 15:653-658. [PMID: 28664036 PMCID: PMC5479963 DOI: 10.1016/j.nicl.2017.06.010] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 05/28/2017] [Accepted: 06/08/2017] [Indexed: 11/16/2022]
Abstract
Background Diagnostic uncertainty in ALS has serious management implications and delays recruitment into clinical trials. Emerging evidence of presymptomatic disease-burden provides the rationale to develop diagnostic applications based on the evaluation of in-vivo pathological patterns early in the disease. Objectives To outline and test a diagnostic classification approach based on an array of complementary imaging metrics in key disease-associated anatomical structures. Methods Data from 75 ALS patients and 75 healthy controls were randomly allocated in a ‘training’ and ‘validation’ cohort. Spatial masks were created for anatomical foci which best discriminate patients from controls in the ‘training sample’. In a virtual ‘brain biopsy’, data was then retrieved from these key disease-associated brain regions. White matter diffusivity indices, grey matter T1-signal intensity values and basal ganglia volumes were evaluated as predictor variables in a canonical discriminant function. Results Following predictor variable selection, a classification specificity of 85.5% and sensitivity of 89.1% was achieved in the training sample and 90% specificity and 90% sensitivity in the validation sample. Discussion This study evaluates disease-associated imaging measures in a dummy diagnostic application. Although larger samples will be required for robust validation, the study confirms the potential of multimodal quantitative imaging in future clinical applications. Reliable diagnostic, monitoring and prognostic biomarkers are urgently in ALS. Accurate diagnostic classification may be achieved based on MRI metrics. Basal ganglia, grey and white matter indices were integrated in a diagnostic model. 85.5% specificity and 89.1% sensitivity were achieved in the training sample. 90% specificity and 90% sensitivity were achieved in the validation sample.
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Affiliation(s)
- Peter Bede
- Quantitative Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Ireland.
| | - Parameswaran M Iyer
- Quantitative Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Eoin Finegan
- Quantitative Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Ireland.
| | - Taha Omer
- Quantitative Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Quantitative Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Ireland.
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14
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De Leener B, Mangeat G, Dupont S, Martin AR, Callot V, Stikov N, Fehlings MG, Cohen-Adad J. Topologically preserving straightening of spinal cord MRI. J Magn Reson Imaging 2017; 46:1209-1219. [PMID: 28130805 DOI: 10.1002/jmri.25622] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/18/2016] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To propose a robust and accurate method for straightening magnetic resonance (MR) images of the spinal cord, based on spinal cord segmentation, that preserves spinal cord topology and that works for any MRI contrast, in a context of spinal cord template-based analysis. MATERIALS AND METHODS The spinal cord curvature was computed using an iterative Non-Uniform Rational B-Spline (NURBS) approximation. Forward and inverse deformation fields for straightening were computed by solving analytically the straightening equations for each image voxel. Computational speed-up was accomplished by solving all voxel equation systems as one single system. Straightening accuracy (mean and maximum distance from straight line), computational time, and robustness to spinal cord length was evaluated using the proposed and the standard straightening method (label-based spline deformation) on 3T T2 - and T1 -weighted images from 57 healthy subjects and 33 patients with spinal cord compression due to degenerative cervical myelopathy (DCM). RESULTS The proposed algorithm was more accurate, more robust, and faster than the standard method (mean distance = 0.80 vs. 0.83 mm, maximum distance = 1.49 vs. 1.78 mm, time = 71 vs. 174 sec for the healthy population and mean distance = 0.65 vs. 0.68 mm, maximum distance = 1.28 vs. 1.55 mm, time = 32 vs. 60 sec for the DCM population). CONCLUSION A novel image straightening method that enables template-based analysis of quantitative spinal cord MRI data is introduced. This algorithm works for any MRI contrast and was validated on healthy and patient populations. The presented method is implemented in the Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1209-1219.
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Affiliation(s)
- Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Gabriel Mangeat
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Sara Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Allan R Martin
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Virginie Callot
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.,AP-HM, Hopital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
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15
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El Mendili MM, Lenglet T, Stojkovic T, Behin A, Guimarães-Costa R, Salachas F, Meininger V, Bruneteau G, Le Forestier N, Laforêt P, Lehéricy S, Benali H, Pradat PF. Cervical Spinal Cord Atrophy Profile in Adult SMN1-Linked SMA. PLoS One 2016; 11:e0152439. [PMID: 27089520 PMCID: PMC4835076 DOI: 10.1371/journal.pone.0152439] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 03/14/2016] [Indexed: 12/14/2022] Open
Abstract
PURPOSE The mechanisms underlying the topography of motor deficits in spinal muscular atrophy (SMA) remain unknown. We investigated the profile of spinal cord atrophy (SCA) in SMN1-linked SMA, and its correlation with the topography of muscle weakness. MATERIALS AND METHODS Eighteen SMN1-linked SMA patients type III/V and 18 age/gender-matched healthy volunteers were included. Patients were scored on manual muscle testing and functional scales. Spinal cord was imaged using 3T MRI system. Radial distance (RD) and cord cross-sectional area (CSA) measurements in SMA patients were compared to those in controls and correlated with strength and disability scores. RESULTS CSA measurements revealed a significant cord atrophy gradient mainly located between C3 and C6 vertebral levels with a SCA rate ranging from 5.4% to 23% in SMA patients compared to controls. RD was significantly lower in SMA patients compared to controls in the anterior-posterior direction with a maximum along C4 and C5 vertebral levels (p-values < 10-5). There were no correlations between atrophy measurements, strength and disability scores. CONCLUSIONS Spinal cord atrophy in adult SMN1-linked SMA predominates in the segments innervating the proximal muscles. Additional factors such as neuromuscular junction or intrinsic skeletal muscle defects may play a role in more complex mechanisms underlying weakness in these patients.
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Affiliation(s)
- Mohamed-Mounir El Mendili
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, France
| | - Timothée Lenglet
- APHP, Hôpital Pitié-Salpêtriere, Département des Maladies du Système Nerveux, Centre référent SLA, Paris, France
- APHP, Hôpital Pitié-Salpêtriere, Service d’Explorations Fonctionnelles, Paris, France
| | - Tanya Stojkovic
- APHP, Centre de Référence Maladies Neuromusculaires Paris-Est, Institut de Myologie, Paris, France
| | - Anthony Behin
- APHP, Centre de Référence Maladies Neuromusculaires Paris-Est, Institut de Myologie, Paris, France
| | - Raquel Guimarães-Costa
- APHP, Centre de Référence Maladies Neuromusculaires Paris-Est, Institut de Myologie, Paris, France
| | - François Salachas
- APHP, Hôpital Pitié-Salpêtriere, Département des Maladies du Système Nerveux, Centre référent SLA, Paris, France
| | - Vincent Meininger
- APHP, Hôpital Pitié-Salpêtriere, Département des Maladies du Système Nerveux, Centre référent SLA, Paris, France
| | - Gaelle Bruneteau
- APHP, Hôpital Pitié-Salpêtriere, Département des Maladies du Système Nerveux, Centre référent SLA, Paris, France
| | - Nadine Le Forestier
- APHP, Hôpital Pitié-Salpêtriere, Département des Maladies du Système Nerveux, Centre référent SLA, Paris, France
| | - Pascal Laforêt
- APHP, Centre de Référence Maladies Neuromusculaires Paris-Est, Institut de Myologie, Paris, France
| | - Stéphane Lehéricy
- APHP, Hôpital Pitié-Salpêtriere, Service de Neuroradiologie, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06, UMR-S975, Inserm U975, CNRS UMR7225, Centre de recherche de l’Institut du Cerveau et de la Moelle épinière–CRICM, Centre de Neuroimagerie de Recherche–CENIR, Paris, France
| | - Habib Benali
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, France
| | - Pierre-François Pradat
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, France
- APHP, Hôpital Pitié-Salpêtriere, Département des Maladies du Système Nerveux, Centre référent SLA, Paris, France
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16
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De Leener B, Taso M, Cohen-Adad J, Callot V. Segmentation of the human spinal cord. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2016; 29:125-53. [PMID: 26724926 DOI: 10.1007/s10334-015-0507-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 11/03/2015] [Accepted: 11/03/2015] [Indexed: 12/14/2022]
Abstract
Segmenting the spinal cord contour is a necessary step for quantifying spinal cord atrophy in various diseases. Delineating gray matter (GM) and white matter (WM) is also useful for quantifying GM atrophy or for extracting multiparametric MRI metrics into specific WM tracts. Spinal cord segmentation in clinical research is not as developed as brain segmentation, however with the substantial improvement of MR sequences adapted to spinal cord MR investigations, the field of spinal cord MR segmentation has advanced greatly within the last decade. Segmentation techniques with variable accuracy and degree of complexity have been developed and reported in the literature. In this paper, we review some of the existing methods for cord and WM/GM segmentation, including intensity-based, surface-based, and image-based methods. We also provide recommendations for validating spinal cord segmentation techniques, as it is important to understand the intrinsic characteristics of the methods and to evaluate their performance and limitations. Lastly, we illustrate some applications in the healthy and pathological spinal cord. One conclusion of this review is that robust and automatic segmentation is clinically relevant, as it would allow for longitudinal and group studies free from user bias as well as reproducible multicentric studies in large populations, thereby helping to further our understanding of the spinal cord pathophysiology and to develop new criteria for early detection of subclinical evolution for prognosis prediction and for patient management. Another conclusion is that at the present time, no single method adequately segments the cord and its substructure in all the cases encountered (abnormal intensities, loss of contrast, deformation of the cord, etc.). A combination of different approaches is thus advised for future developments, along with the introduction of probabilistic shape models. Maturation of standardized frameworks, multiplatform availability, inclusion in large suite and data sharing would also ultimately benefit to the community.
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Affiliation(s)
- Benjamin De Leener
- Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Manuel Taso
- Aix Marseille Université, IFSTTAR, LBA UMR_T 24, Marseille, France.,Aix Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.,APHM, Hôpital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France
| | - Julien Cohen-Adad
- Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Virginie Callot
- Aix Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France. .,APHM, Hôpital de la Timone, Pôle d'imagerie médicale, CEMEREM, Marseille, France.
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17
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18
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De Leener B, Cohen-Adad J, Kadoury S. Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1705-1718. [PMID: 26011879 DOI: 10.1109/tmi.2015.2437192] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g., T1-, T2- and T2(∗) -weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter- and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 ± 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts.
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