Open-source pipeline for multi-class segmentation of the spinal cord with deep learning.

Journal: Magnetic resonance imaging
PMID: 31004711

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.

Authors

  • François Paugam
    École Centrale de Lyon, Lyon, France; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada. Electronic address: francois.paugam@laposte.net.
  • Jennifer Lefeuvre
    National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA.
  • Christian S Perone
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
  • Charley Gros
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
  • Daniel S Reich
  • Pascal Sati
    Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
  • Julien Cohen-Adad
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.

Keywords

External Resources

Popular Topics
Recent Journals