Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data

PLoS One. 2015 Oct 7;10(10):e0139323. doi: 10.1371/journal.pone.0139323. eCollection 2015.

Abstract

Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Imaging, Three-Dimensional / methods
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Spinal Cord / anatomy & histology*
  • Spinal Cord / pathology
  • Spinal Cord / ultrastructure*
  • Young Adult

Grant support

The authors are grateful to the Craig H. Neilsen Foundation (http://chnfoundation.org/mDefault.aspx?Section=home) for funding for this research. MGF is supported by the Gerald and Tootsie Halbert Chair in Neural Repair and Regeneration and by funding from Phillip and Peggy DeZwirek. The funders had no role in the design, data collection, data analysis, decision to publish or preparation of the manuscript.