Abstract
Label fusion is a key step in multi-atlas based segmentation, which combines labels from multiple atlases to make the final decision. However, most of the current label fusion methods consider each voxel equally and independently during label fusion. In our point of view, however, different voxels act different roles in the way that some voxels might have much higher confidence in label determination than others, i.e., because of their better alignment across all registered atlases. In light of this, we propose a sequential label fusion framework for multi-atlas based image segmentation by hierarchically using the voxels with high confidence to guide the labeling procedure of other challenging voxels (whose registration results among deformed atlases are not good enough) to afford more accurate label fusion. Specifically, we first measure the corresponding labeling confidence for each voxel based on the k-nearest-neighbor rule, and then perform label fusion sequentially according to the estimated labeling confidence on each voxel. In particular, for each label fusion process, we use not only the propagated labels from atlases, but also the estimated labels from the neighboring voxels with higher labeling confidence. We demonstrate the advantage of our method by deploying it to the two popular label fusion algorithms, i.e., majority voting and local weighted voting. Experimental results show that our sequential label fusion method can consistently improve the performance of both algorithms in terms of segmentation/labeling accuracy.
Chapter PDF
Similar content being viewed by others
References
Lotjonen, J., Wolz, R., Koikkalainen, J., Thurfjell, L., Waldemar, G., Soininen, H., Rueckert, D.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49, 2352–2365 (2010)
Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M.A., van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med.l Imag. 28, 1000–1010 (2009)
Langerak, T.R., van der Heide, U.A., Kotte, A.N., Viergever, M.A., van Vulpen, M., Pluim, J.P.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med.l Imag. 29, 2000–2008 (2010)
Artaechevarria, X., Munoz-Barrutia, A., de Solorzano, C.O.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Trans. Med.l Imag. 28, 1266–1277 (2009)
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage 46, 726–738 (2009)
Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. Med.l Imag. 29, 1714–1729 (2010)
Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54, 940–954 (2011)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Inc., Chichester (2000)
Christensen, G., Geng, X., Kuhl, J., Bruss, J., Grabowski, T., Pirwani, I., Vannier, M., Allen, J., 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)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med.l Imag. 23, 903–921 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Wu, G., Jia, H., Shen, D. (2011). Confidence-Guided Sequential Label Fusion for Multi-atlas Based Segmentation. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-23626-6_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23625-9
Online ISBN: 978-3-642-23626-6
eBook Packages: Computer ScienceComputer Science (R0)