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

Adam Cadotte, David W. Cadotte, Micha Livne, Julien Cohen-Adad, David Fleet, David Mikulis and Michael G. Fehlings

Article (2015)

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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.

Uncontrolled Keywords

Adult; Algorithms; Female; Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Male; Middle Aged; Spinal Cord; Young Adult

Subjects: 1900 Biomedical engineering > 1900 Biomedical engineering
2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
9000 Health sciences > 9000 Health sciences
Department: Institut de génie biomédical
Funders: Craig H. Neilsen Foundation, Gerald and Tootsie Halbert Chair in Neural Repair and Regeneration, Phillip and Peggy DeZwirek
PolyPublie URL: https://publications.polymtl.ca/3484/
Journal Title: PLOS One (vol. 10, no. 10)
Publisher: PLOS
DOI: 10.1371/journal.pone.0139323
Official URL: https://doi.org/10.1371/journal.pone.0139323
Date Deposited: 23 Nov 2018 10:31
Last Modified: 08 Apr 2024 18:53
Cite in APA 7: Cadotte, A., Cadotte, D. W., Livne, M., Cohen-Adad, J., Fleet, D., Mikulis, D., & Fehlings, M. G. (2015). Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data. PLOS One, 10(10). https://doi.org/10.1371/journal.pone.0139323


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