Abstract
Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898±0.019 Dice overlap to manual labelings for controls.
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References
Allassonniere, S., Amit, Y., Trouve, A.: Towards a coherent statistical framework for dense deformable template estimation. Journal of the Royal Statistical Society: Series B 69(1), 3–29 (2007)
Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Tran. Medical Imaging 28(8), 1266–1277 (2009)
Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis 12, 26–41 (2008)
Collins, D., Pruessner, J.: Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. NeuroImage 52(4), 1355–1366 (2010)
Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Nonlocal patch-based label fusion for hippocampus segmentation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 129–136. Springer, Heidelberg (2010)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Hasboun, D., Chantome, M., Zouaoui, A., Sahel, M., Deladoeuille, M., Sourour, N., Duymes, M., Baulac, M., Marsault, C., Dormont, D.: MR determination of hippocampal volume: Comparison of three methods. Am. J. Neuroradiol. 17, 1091–1098 (1996)
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphism atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)
Kittler, J.: Combining classifiers: A theoretical framework. Pattern Analysis and Application 1, 18–27 (1998)
Leung, K., Barnes, J., Ridgway, G., Bartlett, J., Clarkson, M., Macdonald, K., Schuff, N., Fox, N., Ourselin, S.: Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s Disease. NeuroImage 51, 1345–1359 (2010)
Murty, K.G.: Linear Complementarity, Linear and Nonlinear Programming. Helderman-Verlag (1988)
Pluta, J., Avants, B., Glynn, S., Awate, S., Gee, J., Detre, J.: Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation. Hippocampus 19, 565–571 (2009)
Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4), 1428–1442 (2004)
Sabuncu, M., Yeo, B., Leemput, K.V., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. on Medical Imaging 29(10), 1714–1720 (2010)
Scahill, R., Schott, J., Stevens, J., Fox, M.R.N.: Mapping the evolution of regional atrophy in Alzheimer’s Disease: unbiased analysis of fluidregistered serial MRI. Proc. Natl. Acad. Sci. U. S. A. 99(7), 4703–4707 (2002)
Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. on Medical Imaging 23(7), 903–921 (2004)
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Wang, H., Suh, J.W., Pluta, J., Altinay, M., Yushkevich, P. (2011). Optimal Weights for Multi-atlas Label Fusion. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_7
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DOI: https://doi.org/10.1007/978-3-642-22092-0_7
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