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Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging.

Publication ,  Journal Article
Peterson, DE; Chen, SD; Calabrese, E; White, LE; Provenzale, JM
Published in: Neuroradiol J
February 2016

The goal of this study was to apply image registration-based automated segmentation methods to measure diffusion tensor imaging (DTI) metrics within the canine brain. Specifically, we hypothesized that this method could measure DTI metrics within the canine brain with greater reproducibility than with hand-drawn region of interest (ROI) methods. We performed high-resolution post-mortem DTI imaging on two canine brains on a 7 T MR scanner. We designated the two brains as brain 1 and brain 2. We measured DTI metrics within the corpus callosum of brain 1 using a hand-drawn ROI method and an automated segmentation method in which ROIs from brain 2 were transformed into the space of brain 1. We repeated both methods in order to measure their reliability. Mean differences between the two sets of hand-drawn ROIs ranged from 4% to 10%. Mean differences between the hand-drawn ROIs and the automated ROIs were less than 3%. The mean differences between the first and second automated ROIs were all less than 0.25%. Our findings indicate that the image registration-based automated segmentation method was clearly the more reproducible method. These results provide the groundwork for using image registration-based automated segmentation methods to measure DTI metrics within the canine brain. Such methods will facilitate the study of white matter pathology in canine models of neurologic disease.

Duke Scholars

Published In

Neuroradiol J

DOI

ISSN

1971-4009

Publication Date

February 2016

Volume

29

Issue

1

Start / End Page

4 / 12

Location

United States

Related Subject Headings

  • Subtraction Technique
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Machine Learning
  • In Vitro Techniques
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Dogs
 

Citation

APA
Chicago
ICMJE
MLA
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Peterson, D. E., Chen, S. D., Calabrese, E., White, L. E., & Provenzale, J. M. (2016). Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging. Neuroradiol J, 29(1), 4–12. https://doi.org/10.1177/1971400915610924
Peterson, David E., Steven D. Chen, Evan Calabrese, Leonard E. White, and James M. Provenzale. “Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging.Neuroradiol J 29, no. 1 (February 2016): 4–12. https://doi.org/10.1177/1971400915610924.
Peterson DE, Chen SD, Calabrese E, White LE, Provenzale JM. Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging. Neuroradiol J. 2016 Feb;29(1):4–12.
Peterson, David E., et al. “Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging.Neuroradiol J, vol. 29, no. 1, Feb. 2016, pp. 4–12. Pubmed, doi:10.1177/1971400915610924.
Peterson DE, Chen SD, Calabrese E, White LE, Provenzale JM. Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging. Neuroradiol J. 2016 Feb;29(1):4–12.
Journal cover image

Published In

Neuroradiol J

DOI

ISSN

1971-4009

Publication Date

February 2016

Volume

29

Issue

1

Start / End Page

4 / 12

Location

United States

Related Subject Headings

  • Subtraction Technique
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Machine Learning
  • In Vitro Techniques
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Dogs