Fast, Simple, Accurate Multi-atlas Segmentation of the Brain

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8545)


We are concerned with the segmentation of structures within the brain particularly the gyri of the cerebral cortex, but also subcortical structures from volumetric T1-weighted MRI images. A fully automatic multi-atlas registration based segmentation approach is used to label novel data. We use a standard affine registration method combined with a small deformation (non-diffeomorphic), non-linear registration method which optimises mutual information, with a cascading set of regularisation parameters. We consistently segment 138 structures in the brain, 98 in the cortex and 40 in the sub-cortex. An overall Dice score of 0.704 and a mean surface distance of 1.106 mm is achieved in leave-one-out cross validation using all available atlases. The algorithm has been evaluated on a number of different cohorts which includes patients of different ages and scanner manufacturers, and has been shown to be robust. It is shown to have comparable accuracy to other state of the art methods using the MICCAI 2012 multi-atlas challenge benchmark, but the runtime is substantially less.


Mutual Information Registration Method Surface Distance Rigid Registration Joint Histogram 
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  1. 1.
    Petrella, J.R., Coleman, R.E., Doraiswamy, P.M.: Neuroimaging and early diagnosis of Alzheimer disease: a look to the future. Radiology 226, 315–336 (2003)CrossRefGoogle Scholar
  2. 2.
    Andreasen, N.C., Olsen, S.A., Dennert, J.W., Smith, M.R.: Ventricular enlargement in schizophrenia: relationship to positive and negative symptoms. The American Journal of Psychiatry 139, 297–302 (1982)Google Scholar
  3. 3.
    Hutchinson, M., Raff, U.: Structural Changes of the Substantia Nigra in Parkinson’s Disease as Revealed by MR Imaging. Imaging 21, 697–701 (2000)Google Scholar
  4. 4.
    Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J.: A probabilistic atlas of the human brain: theory and rationale for its development the international consortium for brain mapping (ICBM). Neuroimage 2(2PA), 89–101 (1995)Google Scholar
  5. 5.
    MacDonald, D., Kabani, N., Avis, D., Evans, A.C.: Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage 12(3), 340–356 (2000)CrossRefGoogle Scholar
  6. 6.
    Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M.: A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56(3), 907–922 (2011)CrossRefGoogle Scholar
  7. 7.
    Klein, A., Andersson, J., Ardekani, B., Ashburner, J., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786–802 (2009)CrossRefGoogle Scholar
  8. 8.
    Ashburner, J., Friston, K.: Multimodal image coregistration and partitioning–a unified framework. NeuroImage 6(3), 209–217 (1997)CrossRefGoogle Scholar
  9. 9.
    Fischl, B.: Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex 14(1), 11–22 (2004)CrossRefGoogle Scholar
  10. 10.
    Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21, 1428–1442 (2004)CrossRefGoogle Scholar
  11. 11.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation 23(7), 903–921 (2004)Google Scholar
  12. 12.
    Landman, B., Warfield, S.: MICCAI 2012 workshop on multi-atlas labeling.. Challenge and Workshop on Multi-Atlas Labeling …(2012)Google Scholar
  13. 13.
    Wang, H., Das, S.R., Suh, J.W., Altinay, M., Pluta, J., Craige, C., Avants, B., Yushkevich, P.A.: A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation. NeuroImage 55(3), 968–985 (2011)CrossRefGoogle Scholar
  14. 14.
    Powell, M.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal 7(2), 155 (1964)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association (1963)Google Scholar
  17. 17.
    Crum, W.R., Hill, D.L.G., Hawkes, D.J.: Information theoretic similarity measures in non-rigid registration. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 378–387. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Moon, T.: The expectation-maximization algorithm. IEEE Signal Processing Magazine 13 (1996)Google Scholar
  19. 19.
    Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26, 839–851 (2005)CrossRefGoogle Scholar
  20. 20.
    Murgasova, M., Rueckert, D., Edwards, D., Hajnal, J.: Robust segmentation of brain structures in MRI. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (1), pp. 17–20 (June 2009)Google Scholar
  21. 21.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  22. 22.
    Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2010)CrossRefGoogle Scholar
  23. 23.
    Avants, B., Tustison, N., Song, G., Cook, P.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)CrossRefGoogle Scholar
  24. 24.
    Ou, Y., Ye, D.H., Pohl, K.M., Davatzikos, C.: Validation of DRAMMS among 12 popular methods in cross-subject cardiac MRI registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 209–219. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  25. 25.
    Ceritoglu, C., Tang, X., Chow, M., Hadjiabadi, D., Shah, D., Brown, T., Burhanullah, M.H., Trinh, H., Hsu, J.T., Ament, K.A., Crocetti, D., Mori, S., Mostofsky, S.H., Yantis, S., Miller, M.I., Ratnanather, J.T.: Computational analysis of LDDMM for brain mapping. Frontiers in Neuroscience 7, 151 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Toshiba Medical Visualization Systems EuropeEdinburghUK
  2. 2.Department of Diagnostic Imaging and Nuclear MedicineKyoto University Graduate School of MedicineSakyoku, KyotoJapan
  3. 3.Toshiba Medical Systems CorporationOtawaraJapan

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