Abstract
Although multi-atlas segmentation techniques have been producing impressive results for many medical image segmentation problems, most label fusion methods developed so far rely on simple statistical inference models that may not be optimal for inference in high-dimensional feature space. To address this problem, we propose a novel scheme that allows more effective usage of advanced machine learning techniques for patch-based label fusion. Our key novelty is using image registration to guide training sample selection for more effective learning. We demonstrate the power of this new technique in cardiac segmentation using clinical 2D ultrasound images and show superior performance over multi-atlas segmentation and machine learning-based segmentation.
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References
Asman, A., Akhondi-Asl, A., Wang, H., Tustison, N., Avants, B., Warfield, S.K., Landman, B.: Miccai 2013 segmentation algorithms, theory and applications (SATA) challenge results summary. In: MICCAI 2013 Challenge Workshop on Segmentation: Algorithms, Theory and Applications. Springer (2013)
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(1), 26–41 (2008)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Coupe, P., Manjon, J., Fonov, V., Pruessner, J., Robles, N., Collins, D.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)
Han, X.: Learning-boosted label fusion for multi-atlas auto-segmentation. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds.) MLMI 2013. LNCS, vol. 8184, pp. 17–24. Springer, Heidelberg (2013)
Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M., van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion–application to cardiac and aortic segmentation in CT scans. IEEE Trans. on MI 28(7), 1000–1010 (2009)
Landman, B., Warfield, S. (eds.): MICCAI 2012 Workshop on Multi-Atlas Labeling. CreateSpace (2012)
Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002), http://CRAN.R-project.org/doc/Rnews/
Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., Maurer Jr., C.R.: Quo vadis, atlas-based segmentation? In: The Handbook of Medical Image Analysis–Volume III: Registration Models, pp. 435–486 (2005)
Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE TMI 30(10), 1852–1862 (2011)
Sabuncu, M., Yeo, B., Leemput, K.V., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE TMI 29(10), 1714–1720 (2010)
Tu, Z., Zheng, S., Yuille, A., Reiss, A., Dutton, R., Lee, A., Galaburda, A., Dinov, I., Thompson, P., Toga, A.: Automated extraction of the cortical sulci based on a supervised learning approach. IEEE TMI 26(4), 541–552 (2007)
Wang, H., Das, S., Suh, J.W., Altinay, M., Pluta, J., Craige, C., Avants, B., Yushkevich, P.: A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation. NeuroImage 55(3), 968–985 (2011)
Wang, H., Suh, J.W., Das, S., Pluta, J., Altinay, M., Yushkevich, P.: Regression-based label fusion for multi-atlas segmentation. In: CVPR (2011)
Wang, H., Suh, J.W., Das, S., Pluta, J., Craige, C., Yushkevich, P.: Multi-atlas segmentation with joint label fusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 35(3), 611–623 (2013)
Wang, H., Yushkevich, P.A.: Spatial bias in multi-atlas based segmentation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 909–916. IEEE (2012)
Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE TMI 23(7), 903–921 (2004)
Zikic, D., Glocker, B., Criminisi, A.: Atlas encoding by randomized forests for efficient label propagation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 66–73. Springer, Heidelberg (2013)
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Wang, H., Cao, Y., Syeda-Mahmood, T. (2014). Multi-atlas Segmentation with Learning-Based Label Fusion. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_32
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DOI: https://doi.org/10.1007/978-3-319-10581-9_32
Publisher Name: Springer, Cham
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