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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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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