Abstract
Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer’s and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.
Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (www.loni.ucla.edu/ADNI). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au.
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Notes
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Data was collected by the AIBL study group. AIBL study methodology has been reported previously ([4]).
References
Ashburner, J., Friston, K.: Unified segmentation. NeuroImage 26, 839–851 (2005)
Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)
Cardoso, M., Clarkson, M., Ridgway, G., Modat, M., Fox, N., Ourselin, S.: Load: a locally adaptive cortical segmentation algorithm. NeuroImage 56(3), 1386–1397 (2011)
Ellis, K., et al.: The Australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int. Psychogeriatr. 21(04), 672–687 (2009)
Fischl, B., Salat, D., Busa, E., Albert, M., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)
Gao, Y., Liao, S., Shen, D.: Prostate segmentation by sparse representation based classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 451–458. Springer, Heidelberg (2012)
Han, X., Hibbard, L., Oconnell, N., Willcut, V.: Automatic segmentation of parotids in head and neck CT images using multi-atlas fusion. In: MICCAI, pp. 297–304 (2010)
Iglesias, J., Liu, C., Thompson, P., Tu, Z.: Robust brain extraction across datasets and comparison with publicly available methods. TMI 30(9), 1617–1634 (2011)
Liu, X., Montillo, A., Tan, E., Schenck, J.: iSTAPLE: improved label fusion for segmentation by combining STAPLE with image intensity. In: SPIE Medical Imaging (2013)
Mitchell, S., Bosch, J., Lelieveldt, B., van der Geest, R., Reiber, J., Sonka, M.: 3-d active appearance models: segmentation of cardiac MR and ultrasound images. TMI 21(9), 1167–1178 (2002)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2(1), 315–337 (2000)
Rousseau, F., Habas, P., Studholme, C.: A supervised patch-based approach for human brain labeling. TMI 30(10), 1852–1862 (2011)
Shiee, N., Bazin, P.-L., Cuzzocreo, J.L., Blitz, A., Pham, D.L.: Segmentation of brain images using adaptive atlases with application to ventriculomegaly. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 1–12. Springer, Heidelberg (2011)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. TMI 18(10), 897–908 (1999)
Wang, H., Suh, J.W., Das, S.R., Pluta, J., Altinay, M., Yushkevich, P.A.: Regression-based label fusion for multi-atlas segmentation. In: CVPR, pp. 1113–1120 (2011)
Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. TMI 23(7), 903–921 (2004)
Wu, G., Kim, M., Wang, Q., Shen, D.: Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 90–97. Springer, Heidelberg (2012)
Yan, Z., Zhang, S., Liu, X., Metaxas, D., Montillo, A., AIBL: accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer’s disease. In: ISBI (2013)
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Yan, Z., Zhang, S., Liu, X., Metaxas, D.N., Montillo, A., The Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing. (2014). Accurate Whole-Brain Segmentation for Alzheimer’s Disease Combining an Adaptive Statistical Atlas and Multi-atlas. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_7
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