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

Magn Reson Imaging. 2019 Dec;64:21-27. doi: 10.1016/j.mri.2019.04.009. Epub 2019 Apr 17.

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.

Keywords: Deep learning; MRI; Marmoset; Segmentation; Spinal cord; U-Net; cnn.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Callithrix
  • Deep Learning*
  • Gray Matter / diagnostic imaging
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Models, Animal
  • Neural Networks, Computer
  • Spinal Cord / diagnostic imaging*
  • White Matter / diagnostic imaging