ArticleMagnetic resonance imaging2019
Open-source pipeline for multi-class segmentation of the spinal cord with deep learning.
Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
10 citing papers in PubMed.
- Magnetic Resonance Imaging Preprocessing for Robust Spinal Cord Segmentation in Cervical Myelopathy.Journal of imaging · 2026Article
- A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation.Scientific reports · 2025Article
- Semiautomated Pipeline Effectively Assesses Severity and Monitor Disease Progression in Compressed Spinal Cord of Degenerative Cervical Myelopathy Patients.Neurosurgery practice · 2025Article
- Regional spinal cord volumes and pain profiles in AQP4-IgG + NMOSD and MOGAD.Frontiers in neurology · 2024Article
- Attention-gated U-Net networks for simultaneous axial/sagittal planes segmentation of injured spinal cords.Journal of applied clinical medical physics · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Quantitative Measurement of Spinal Cerebrospinal Fluid by Cascade Artificial Intelligence Models in Patients with Spontaneous Intracranial Hypotension.Biomedicines · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system.NeuroImage · 2021Review
- AI in MRI: A case for grassroots deep learning.Magnetic resonance imaging · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or less depending on the homogeneity of the dataset). Two use-case scenarios for segmenting the spinal cord white and grey matter are presented: one in marmosets with variable numbers of lesions, and the other in the publicly available human grey matter segmentation challenge [1]. The pipeline is freely available at: https://github.com/neuropoly/multiclass-segmentation.
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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.