Abstract
Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation.
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References
Anbeek, P., Vincken, K.L., van Osch, M.J., Bisschops, R.H., van der Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21(3), 1037–1044 (2004)
Caligiuri, M.E., Perrotta, P., Augimeri, A., Rocca, F., Quattrone, A., Cherubini, A.: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review. Neuroinformatics 13(3), 261–276 (2015)
Fazekas, F., Chawluk, J.B., Alavi, A., Hurtig, H.I., Zimmerman, R.A.: MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am. J. Neuroradiol. 8(3), 421–426 (1987)
Gibson, E., Gao, F., Black, S.E., Lobaugh, N.J.: Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T. J. Magn. Reson. Imaging 31(6), 1311–1322 (2010)
Ithapu, V., Singh, V., Lindner, C., Austin, B.P., Hinrichs, C., Carlsson, C.M., Bendlin, B.B., Johnson, S.C.: Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies. Hum. Brain Mapp. 35(8), 4219–4235 (2014)
Kikinis, R., Guttmann, C.R., Metcalf, D., Wells, W.M., Ettinger, G.J., Weiner, H.L., Jolesz, F.A.: Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. J. Magn. Reson. Imaging 9(4), 519–530 (1999)
Lao, Z., Shen, D., Liu, D., Jawad, A.F., Melhem, E.R., Launer, L.J., Bryan, R.N., Davatzikos, C.: Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad. Radiol. 15(3), 300–313 (2008)
Ledig, C., Heckemann, R.A., Hammers, A., Lopez, J.C., Newcombe, V.F., Makropoulos, A., Lötjönen, J., Menon, D.K., Rueckert, D.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40–58 (2015)
Liu, W., Hua, G., Smith, J.: Unsupervised one-class learning for automatic outlier removal. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3826–3833 (2014)
Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V.J., Zimmer, C., et al.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4), 3774–3783 (2012)
Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)
Wardlaw, J.M., Smith, E.E., Biessels, G.J., Cordonnier, C., Fazekas, F., Frayne, R., Lindley, R.I., T O’Brien, J., Barkhof, F., Benavente, O.R., et al.: Neuroimaging standards for research into small vessel disease and its contribution to aging and neurodegeneration. Lancet Neurol. 12(8), 822–838 (2013)
Wu, M., Ye, J.: A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2088–2092 (2009)
Yang, F., Shan, Z.Y., Kruggel, F.: White matter lesion segmentation based on feature joint occurrence probability and \(\chi \)2 random field theory from magnetic resonance (MR) images. Pattern Recogn. Lett. 31(9), 781–790 (2010)
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Qin, C. et al. (2016). A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_13
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DOI: https://doi.org/10.1007/978-3-319-47157-0_13
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