Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

McCoy, David; Gros, Charley; Cohen‐Adad, Julien; Huie, J. Russell; Ferguson, Adam R.; Fernandez, Xuan Duong; Thomas, Libby; Singh, Vineeta; Narvid, Jared; Pascual, Lisa U.; Kyritsis, Nikos; Beattie, Michael S.; Bresnahan, Jacqueline C.; Dhall, Sanjay S.; Whetstone, William D.; Talbott, Jason F.; Investigators, Track-Sci

doi:10.3174/ajnr.a6020

Cited by 45 publications

(48 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…20,36,38 We segmented the spinal cord using PropSeg within the Spinal Cord Toolbox. Also, the latest software improvements, such as machine-learning based segmentation methods (eg, DeepSeg within the Spinal Cord Toolbox), 43 could further facilitate the spinal cord image processing. 31,40,41 Also, the possibility of deriving spinal cord GBSI measurements from brain scans needs to be explored.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral

Moccia

Prados

Filippi

et al. 2019

Annals of Neurology

View full text Add to dashboard Cite

Objective: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method. Methods: Baseline and 1-year spinal cord 3-dimensional T1-weighted images (1mm isotropic) were obtained from 282 patients (52 clinically isolated syndrome [CIS], 196 relapsing-remitting MS [RRMS], 34 progressive MS [PMS]), and 82 controls from 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer and multi-field-strength scans. Spinal Cord Toolbox was used for C2-5 segmentation and cross-sectional area (CSA) calculation. After cord straightening and registration, GBSI measured atrophy based on the probabilistic boundary-shift region of interest. CSA and GBSI percentage annual volume change was calculated. Results: GBSI provided similar rates of atrophy, but reduced measurement variability compared to CSA in all MS subtypes (CIS: −0.95 AE 2.11% vs −1.19 AE 3.67%; RRMS: −1.74 AE 2.57% vs −1.74 AE 4.02%; PMS: −2.29 AE 2.40% vs −1.29 AE 3.20%) and healthy controls (0.02 AE 2.39% vs −0.56 AE 3.77%). GBSI performed better than CSA in differentiating healthy controls from CIS (area under the curve [AUC] = 0.66 vs 0.53; p = 0.03), RRMS (AUC = 0.73 vs 0.59; p < 0.001), PMS (AUC = 0.77 vs 0.53; p < 0.001), and patients with disability progression from patients without View this article online at wileyonlinelibrary.com.

show abstract

Section: Discussionmentioning

confidence: 99%

“…This protocol is easy to apply and facilitates the adoption curve of spinal cord MRI acquisitions. Also, the latest software improvements, such as machine‐learning based segmentation methods (eg, DeepSeg within the Spinal Cord Toolbox), could further facilitate the spinal cord image processing.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral

Moccia

Prados

Filippi

et al. 2019

Annals of Neurology

View full text Add to dashboard Cite

show abstract

“…13 McCoy and co-workers stated that targeted convolutional neural network training in SCI improves algorithm performance for this cohort and provides clinically relevant metrics of cord injury. 41 In future studies, we aim to address the following. First, because XGBoost is a method for optimization, an efficient approach needs to be developed to achieve superior prognostic validity.…”

Section: Figmentioning

confidence: 99%

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Inoue

Ichikawa

Ueno

et al. 2020

Neurotrauma Reports

View full text Add to dashboard Cite

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.

show abstract

“…Convolutional neural network-based automated segmentation of the spinal cord and contusion injury: deep learning biomarker correlates of motor impairment in acute spinal cord injury. 29 Developed a convolutional neural network to perform segmentation of the spinal cord in tSCI. Segmentation helped authors conclude that contusion injury volume was significantly correlated with motor scores at admission and discharge.…”

Section: Study Descriptionmentioning

confidence: 99%

“…Like Tay et al, 27 McCoy et al 29 recently applied a ML approach to spinal cord imaging in tSCI. They developed a convolutional neural network (CNN) to perform segmentation of the spinal cord in tSCI.…”

Section: Machine Learning Algorithms In Traumatic Spinal Cord Injurymentioning

confidence: 99%

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

et al. 2019

View full text Add to dashboard Cite

Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.

show abstract

Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

Cited by 45 publications

References 28 publications

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

Contact Info

Product

Resources

About