Abstract
Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling in the non-local manner has been widely investigated to alleviate the possible misalignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases in conventional methods are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these issues, we present a novel patch-based label fusion method in multi-atlas scenario, for the goal of labeling each voxel in the target image by the best representative atlas patches that also have the lowest joint risk of mislabeling. Specifically, sparse coding is used to select a small number of atlas patches which best represent the underlying patch at each point of the target image, thus minimizing the chance of including the misleading atlas patches for labeling. Furthermore, we examine the joint risk of any pair of atlas patches in making similar labeling error, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. This joint risk will be further recursively updated based on the latest labeling results to correct the possible labeling errors. To demonstrate the performance of our proposed method, we have evaluated it on both whole brain parcellation and hippocampus segmentation, and achieved promising labeling results, compared with the state-of- the-art methods.
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References
Coupe, P., et al.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)
Rousseau, F., Habas, P.A., Studholme, C.: A Supervised Patch-Based Approach for Human Brain Labeling. IEEE Trans. Medical Imaging 30(10), 1852–1862 (2011)
Tong, T., et al.: Segmentation of Brain Images via Sparse Patch Representaion. In: STML, Nice, France (2012)
Wang, H., et al.: Multi-Atlas Segmentation with Joint Label Fusion. IEEE Trans. Pattern Anal. Mach. Intell. (2012)
Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: Computer Vision and Pattern Recognition, Beijing, China (2005)
Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287(5456), 1273–1276 (2000)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 58(1), 267–288 (1996)
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)
Wu, T.T., Lange, K.: Coordinate descent algorithms for lasso penalized regression. The Annals of Applied Statistics 2(1), 224–244 (2008)
Christensen, G.E., Geng, X., Kuhl, J.G., Bruss, J., Grabowski, T.J., Pirwani, I.A., Vannier, M.W., Allen, J.S., Damasio, H.: Introduction to the non-rigid image registration evaluation project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006)
Vercauteren, T., et al.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(suppl. 1), S61–S72 (2009)
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Wu, G., Wang, Q., Liao, S., Zhang, D., Nie, F., Shen, D. (2013). Minimizing Joint Risk of Mislabeling for Iterative Patch-Based Label Fusion. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_69
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DOI: https://doi.org/10.1007/978-3-642-40760-4_69
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