• 1. Ashburner J, Csernansky JG, Davatzikos C, et al. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol 2003; 2: 7988.

    • Search Google Scholar
    • Export Citation
  • 2. Bernasconi A, Bernasconi N, Bernhardt BC, et al. Advances in MRI for “cryptogenic” epilepsies. Nat Rev Neurol 2011; 7: 99108.

  • 3. Hudson JA, Simpson ST, Buxton DF, et al. Ultrasonographic diagnosis of canine hydrocephalus. Vet Radiol Ultrasound 1990; 31: 5058.

    • Search Google Scholar
    • Export Citation
  • 4. Carrera I, Dennis R, Mellor DJ, et al. Use of magnetic resonance imaging for morphometric analysis of the caudal cranial fossa in Cavalier King Charles Spaniels. Am J Vet Res 2009; 70: 340345.

    • Search Google Scholar
    • Export Citation
  • 5. Henke D, Böttcher P, Doherr MG, et al. Computer-assisted magnetic resonance imaging brain morphometry in American Staffordshire Terriers with cerebellar cortical degeneration. J Vet Intern Med 2008; 22: 969975.

    • Search Google Scholar
    • Export Citation
  • 6. Thames RA, Robertson ID, Flegel T, et al. Development of a morphometric magnetic resonance image parameter suitable for distinguishing between normal dogs and dogs with cerebellar atrophy. Vet Radiol Ultrasound 2010; 51: 246253.

    • Search Google Scholar
    • Export Citation
  • 7. Roberts T, McGreevy P, Valenzuela M. Human induced rotation and reorganization of the brain of domestic dogs. PLoS ONE 2010;5:e11946.

    • Search Google Scholar
    • Export Citation
  • 8. Tapp PD, Siwak CT, Gao FQ, et al. Frontal lobe volume, function, and beta-amyloid pathology in a canine model of aging. J Neurosci 2004; 24: 82058213.

    • Search Google Scholar
    • Export Citation
  • 9. Su MY, Tapp PD, Vu L, et al. A longitudinal study of brain mor-phometrics using serial magnetic resonance imaging analysis in a canine model of aging. Prog Neuropsychopharmacol Biol Psychiatry 2005; 29: 389397.

    • Search Google Scholar
    • Export Citation
  • 10. Driver CJ, Rusbridge C, Cross HR, et al. Relationship of brain parenchyma within the caudal cranial fossa and ventricle size to syringomyelia in Cavalier King Charles Spaniels. J Small Anim Pract 2010; 51: 382386.

    • Search Google Scholar
    • Export Citation
  • 11. Kuwabara T, Hasegawa D, Kobayashi M, et al. Clinical magnetic resonance volumetry of the hippocampus in 58 epileptic dogs. Vet Radiol Ultrasound 2010; 51: 485490.

    • Search Google Scholar
    • Export Citation
  • 12. Milne ME, Anderson GA, Chow KE, et al. Description of technique and lower reference limit for magnetic resonance imaging of hippocampal volumetry in dogs. Am J Vet Res 2013; 74: 224231.

    • Search Google Scholar
    • Export Citation
  • 13. Schmidt MJ, Amort KH, Failing K, et al. Comparison of the endocranial- and brain volumes in brachycephalic dogs, mesaticephalic dogs and Cavalier King Charles Spaniels in relation to their body weight. Acta Vet Scand 2014;56:30.

    • Search Google Scholar
    • Export Citation
  • 14. Datta R, Lee J, Duda J, et al. A digital atlas of the dog brain. PloS ONE 2012;7:e52140.

  • 15. Jacqmot O, Van Thielen B, Fierens Y. Diffusion tensor imaging of white matter tracts in the dog brain. Anat Rec (Hoboken) 2013; 296: 340349.

    • Search Google Scholar
    • Export Citation
  • 16. Tapp PD, Head K, Head E, et al. Application of an automated voxel-based morphometry technique to assess regional gray and white matter brain atrophy in a canine model of aging. Neuroimage 2006; 29: 234244.

    • Search Google Scholar
    • Export Citation
  • 17. Jia H, Pustovyy OM, Waggoner P, et al. Functional MRI of the olfactory system in conscious dogs. PLoS ONE 2014;9:e86362.

  • 18. Cabezas M, Oliver A, Lladó X, et al. A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Programs Biomed 2011; 104:e158e177.

    • Search Google Scholar
    • Export Citation
  • 19. Evans HE, Miller ME. Miller's anatomy of the dog. 3rd ed. Philadelphia: WB Saunders Co, 1993.

  • 20. Jenkinson M, Beckmann CF, Behrens TEJ, et al. FSL. Neuroimage 2012; 62: 782790.

  • 21. Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006; 31: 11161128.

    • Search Google Scholar
    • Export Citation
  • 22. Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011; 54: 20332044.

    • Search Google Scholar
    • Export Citation
  • 23. Avants BB, Epstein CL, Grossman M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008; 12: 2641.

    • Search Google Scholar
    • Export Citation
  • 24. Avants B, Gee JC. Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 2004; 23(suppl 1): S139S150.

    • Search Google Scholar
    • Export Citation
  • 25. Tustison NJ, Gee JC. Introducing Dice, Jaccard, and other label overlap measures to ITK. Insight J 2009;Jul-Dec:707.

  • 26. Onar V, Ozcan S, Pazvant G. Skull typology of adult male Kangal dogs. Anat Histol Embryol 2001; 30: 4148.

  • 27. Schmidt MJ, Neumann AC, Amort KH, et al. Cephalometric measurements and determination of general skull type of Cavalier King Charles Spaniels. Vet Radiol Ultrasound 2011; 52: 436440.

    • Search Google Scholar
    • Export Citation
  • 28. Navon D. Forest before trees: the precedence of global features in visual perception. Cognit Psychol 1977; 9: 353383.

Advertisement

Development of representative magnetic resonance imaging–based atlases of the canine brain and evaluation of three methods for atlas-based segmentation

View More View Less
  • 1 Department of Radiology, University of Melbourne Veterinary Hospital, Werribee, VIC 3030, Australia.
  • | 2 Department of Radiology, Royal Melbourne Hospital, 300 Grattan St, Parkville, VIC 3050, Australia.
  • | 3 Department of Asia-Pacific Centre for Animal Health, Faculty of Veterinary and Agricultural Science, University of Melbourne, Werribee, VIC 3030, Australia.
  • | 4 Department of Neurology and Neurosurgery, University of Melbourne Veterinary Hospital, Werribee, VIC 3030, Australia.
  • | 5 Department of Medicine, University of Melbourne, Parkville, VIC 3050, Australia.
  • | 6 Department of Melbourne Brain Centre, University of Melbourne, Parkville, VIC 3050, Australia.

Abstract

OBJECTIVE To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS).

ANIMALS 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease.

PROCEDURES The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape–specific template [A], automatic brain extraction and application of a brain shape–specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard.

RESULTS Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97.

CONCLUSIONS AND CLINICAL RELEVANCE Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

Abstract

OBJECTIVE To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS).

ANIMALS 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease.

PROCEDURES The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape–specific template [A], automatic brain extraction and application of a brain shape–specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard.

RESULTS Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97.

CONCLUSIONS AND CLINICAL RELEVANCE Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

Supplementary Materials

    • Supplementary Figures and Table (PDF 122 kb)

Contributor Notes

Address correspondence to Dr. Milne (mmil@unimelb.edu.au).