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

Cadotte A, Cadotte DW, Livne M, Cohen-Adad J, Fleet D, Mikulis D, Fehlings MG - PLoS ONE (2015)

Bottom Line: Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.

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.

No MeSH data available.


Related in: MedlinePlus

Example of the difference between two raters segmentation of a smoothed T2-weighted MR image.The green region represents the area segmented by Rater 1 and the red region is Rater 2 less Rater 1. Although the segmentations are very similar, and both could subjectively be considered accurate, there is an 8 mm2, or 9%, difference in CSA.
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pone.0139323.g007: Example of the difference between two raters segmentation of a smoothed T2-weighted MR image.The green region represents the area segmented by Rater 1 and the red region is Rater 2 less Rater 1. Although the segmentations are very similar, and both could subjectively be considered accurate, there is an 8 mm2, or 9%, difference in CSA.

Mentions: In Fig 7, we illustrate the difference in segmentation between two manual segmentations. The area in green represents the area marked by Rater 1 and the area in red represents Rater 2 minus Rater 1. Although this difference looks small, it represents an 8 mm2 CSA difference, or approximately 10% of the CSA.


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

Cadotte A, Cadotte DW, Livne M, Cohen-Adad J, Fleet D, Mikulis D, Fehlings MG - PLoS ONE (2015)

Example of the difference between two raters segmentation of a smoothed T2-weighted MR image.The green region represents the area segmented by Rater 1 and the red region is Rater 2 less Rater 1. Although the segmentations are very similar, and both could subjectively be considered accurate, there is an 8 mm2, or 9%, difference in CSA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596853&req=5

pone.0139323.g007: Example of the difference between two raters segmentation of a smoothed T2-weighted MR image.The green region represents the area segmented by Rater 1 and the red region is Rater 2 less Rater 1. Although the segmentations are very similar, and both could subjectively be considered accurate, there is an 8 mm2, or 9%, difference in CSA.
Mentions: In Fig 7, we illustrate the difference in segmentation between two manual segmentations. The area in green represents the area marked by Rater 1 and the area in red represents Rater 2 minus Rater 1. Although this difference looks small, it represents an 8 mm2 CSA difference, or approximately 10% of the CSA.

Bottom Line: Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.

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.

No MeSH data available.


Related in: MedlinePlus