Abstract
In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated group-mean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.
This work was supported in part by National Natural Science Foundation of China (No. 61503300) and China Postdoctoral Science Foundation (No. 2014M560801).
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References
Bird, C.M., Burgess, N.: The hippocampus and memory: insights from spatial processing. Nat. Rev. Neurosci. 9(3), 182–194 (2008)
Schuff, N., et al.: MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain 132(4), 1067–1077 (2009)
Schröder, J., Pantel, J.: Neuroimaging of hippocampal atrophy in early recognition of Alzheimer’s disease - a critical appraisal after two decades of research. Psychiatry Res.: Neuroimaging 247, 71–78 (2016)
van der Lijn, F., et al.: Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. NeuroImage 43(4), 708–720 (2008)
Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Trans. Med. Imaging 30(10), 1852–1862 (2011)
Zhang, D., Guo, Q., Wu, G., Shen, D.: Sparse patch-based label fusion for multi-atlas segmentation. In: Yap, P.-T., Liu, T., Shen, D., Westin, C.-F., Shen, L. (eds.) MBIA 2012. LNCS, vol. 7509, pp. 94–102. Springer, Heidelberg (2012)
Zarpalas, D., et al.: Gradient-based reliability maps for ACM-based segmentation of hippocampus. IEEE Trans. Biomed. Eng. 61(4), 1015–1026 (2014)
Song, Y., Wu, G., Sun, Q., Bahrami, K., Li, C., Shen, D.: Progressive label fusion framework for multi-atlas segmentation by dictionary evolution. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 190–197. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24574-4_23
Wolz, R., et al.: Measurement of hippocampal atrophy using 4D graph-cut segmentation: application to ADNI. NeuroImage 52(1), 109–118 (2010)
Leung, K.K., et al.: Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. NeuroImage 51(4), 1345–1359 (2010)
Guo, Y., Wu, G., Yap, P.-T., Jewells, V., Lin, W., Shen, D.: Segmentation of infant hippocampus using common feature representations learned for multimodal longitudinal data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 63–71. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24574-4_8
Chincarini, A., et al.: Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. NeuroImage 125, 834–847 (2016)
Wu, G., Wang, Q., Shen, D.: Registration of longitudinal brain image sequences with implicit template and spatial-temporal heuristics. NeuroImage 59(1), 404–421 (2012)
Jenkinson, M., et al.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2), 825–841 (2002)
Vercauteren, T., et al.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)
Karacali, B., Davatzikos, C.: Simulation of tissue atrophy using a topology preserving transformation model. IEEE Trans. Med. Imaging 25(5), 649–652 (2006)
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Wang, L., Guo, Y., Cao, X., Wu, G., Shen, D. (2016). Consistent Multi-Atlas Hippocampus Segmentation for Longitudinal MR Brain Images with Temporal Sparse Representation. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2016. Lecture Notes in Computer Science(), vol 9993. Springer, Cham. https://doi.org/10.1007/978-3-319-47118-1_5
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DOI: https://doi.org/10.1007/978-3-319-47118-1_5
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