Abstract
In image-based medical research, atlases are widely used in many tasks, for example, spatial normalization and segmentation. If atlases are regarded as representative patterns for a population of images, then multiple atlases are required for a heterogeneous population. In conventional atlas construction methods, the “unit” of representative patterns is images. Every input image is associated with its most similar atlas. As the number of subjects increases, the heterogeneity increases accordingly, and a big number of atlases may be needed. In this paper, we explore using region-wise, instead of image-wise, patterns to represent a population. Different parts of an input image is fuzzily associated with different atlases according to voxel-level association weights. In this way, regional structure patterns from different atlases can be combined together. Based on this model, we design a variational framework for multi-atlas construction. In the application to two T1-weighted MRI data sets, the method shows promising performance, in comparison with a conventional unbiased atlas construction method.
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References
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(Suppl. 1), S151–S160 (2004)
Park, H., Bland, P.H., Hero III, A.O., Meyer, C.R.: Least biased target selection in probabilistic atlas construction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 419–426. Springer, Heidelberg (2005)
Seghers, D., D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P.: Construction of a brain template from Mr images using state-of-the-art registration and segmentation techniques. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 696–703. Springer, Heidelberg (2004)
Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: Gram: A framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14(5), 633–642 (2010)
Jia, H., Wu, G., Wang, Q., Shen, D.: Absorb: Atlas building by self-organized registration and bundling. Neuroimage 51(3), 1057–1070 (2010)
Wang, Q., Chen, L., Yap, P.-T., Wu, G., Shen, D.: Groupwise registration based on hierarchical image clustering and atlas synthesis. Hum. Brain Mapp. 31(8), 1128–1140 (2010)
Wu, G., Jia, H., Wang, Q., Shen, D.: Sharpmean: groupwise registration guided by sharp mean image and tree-based registration. Neuroimage 56(4), 1968–1981 (2011)
Blezek, D.J., Miller, J.V.: Atlas stratification. Med. Image Anal. 11(5), 443–457 (2007)
Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.: Image-driven population analysis through mixture modeling. IEEE Transactions on Medical Imaging 28(9), 1473–1487 (2009)
Xie, Y., Ho, J., Vemuri, B.: Multiple atlas construction from a heterogeneous brain MR image collection. IEEE Transactions on Medical Imaging PP(99), 1 (2013)
Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064–1080 (2008)
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Li, J., Shi, Y., Dinov, I.D., Toga, A.W. (2013). Locally Weighted Multi-atlas Construction. In: Shen, L., Liu, T., Yap, PT., Huang, H., Shen, D., Westin, CF. (eds) Multimodal Brain Image Analysis. MBIA 2013. Lecture Notes in Computer Science, vol 8159. Springer, Cham. https://doi.org/10.1007/978-3-319-02126-3_1
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DOI: https://doi.org/10.1007/978-3-319-02126-3_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02125-6
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