ArticleAJNR. American journal of neuroradiology2019
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.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04565366 (Early Critical Care Decisions and Outcomes After SCI), which is not on this map. Cited by 38 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Critical Care Decisions and Outcomes After SCI: TRACK-SCI
Who cites it
38 citing papers in PubMed, 75 citations in OpenAlex.
- Magnetic Resonance Imaging Preprocessing for Robust Spinal Cord Segmentation in Cervical Myelopathy.Journal of imaging · 2026Article
- Severity Prediction of Traumatic Cervical Spinal Cord Injury With an AI Model Based on MRI Radiomics.JOR spine · 2026Article
- Spinal Cord Segmentation and Injury Detection based on Siamese Conventional WideRes Network using CT Image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Building a library of acute traumatic spinal cord injury images across Canada: a retrospective cohort study protocol.BMJ open · 2025Article
- Noise-robust markerless video gait anomaly detection via two-stage acquisition and LSTM autoencoders.Scientific reports · 2025Article
- Magnetization transfer MRI (MT-MRI) detects white matter damage beyond the primary site of compression in degenerative cervical myelopathy using a novel semi-automated analysis.Computers in biology and medicine · 2025Article
- Determination of Diagnosis and Prognosis in Spinal Cord Injury Using Machine Learning.Korean journal of neurotrauma · 2025Review
- Lesion characteristics are associated with bowel, bladder, and overall independence following cervical spinal cord injury.The journal of spinal cord medicine · 2025Article
- Fractal-Based Architectures with Skip Connections and Attention Mechanism for Improved Segmentation of MS Lesions in Cervical Spinal Cord.Diagnostics (Basel, Switzerland) · 2025Article
- The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review.Brain & spine · 2025Review
- SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.Radiology. Artificial intelligence · 2025Article
- Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation.Frontiers in rehabilitation sciences · 2025Article
- Utilizing multimodal radiomics technology from cervical MRI for diagnosis of cervical spinal cord injury and spinal cord concussion.Scientific reports · 2024Article
- Advances and Challenges in Spinal Cord Injury Treatments.Journal of clinical medicine · 2024Review
- Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.Journal of applied clinical medical physics · 2024Article
- SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury.medRxiv : the preprint server for health sciences · 2024Article
- Deep Learning-Based Prediction Model for Gait Recovery after a Spinal Cord Injury.Diagnostics (Basel, Switzerland) · 2024Article
- Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients.Journal of clinical medicine · 2024Article
- Recent trends in spinal trauma management and research.Journal of clinical orthopaedics and trauma · 2024Article
- Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeOur aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR imaging in a cohort of patients with acute spinal cord injury. MATERIALS AND
methodsForty-seven patients who underwent 3T MR imaging within 24 hours of spinal cord injury were included. We developed an image-analysis pipeline integrating 2D convolutional neural networks for whole spinal cord and intramedullary spinal cord lesion segmentation. Linear mixed modeling was used to compare test segmentation results between our spinal cord injury convolutional neural network (Brain and Spinal Cord Injury Center segmentation) and current state-of-the-art methods. Volumes of segmented lesions were then used in a linear regression analysis to determine associations with motor scores.
resultsCompared with manual labeling, the average test set Dice coefficient for the Brain and Spinal Cord Injury Center segmentation model was 0.93 for spinal cord segmentation versus 0.80 for PropSeg and 0.90 for DeepSeg (both components of the Spinal Cord Toolbox). Linear mixed modeling showed a significant difference between Brain and Spinal Cord Injury Center segmentation compared with PropSeg (P < .001) and DeepSeg (
conclusionsBrain and Spinal Cord Injury Center segmentation of the spinal cord compares favorably with available segmentation tools in a population with acute spinal cord injury. Volumes of injury derived from automated lesion segmentation with Brain and Spinal Cord Injury Center segmentation correlate with measures of motor impairment in the acute phase. Targeted convolutional neural network training in acute spinal cord injury enhances algorithm performance for this patient population and provides clinically relevant metrics of cord injury.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.