A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation
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.
KeywordsDementia With Lewy Body White Matter Hyperintensities Label Data Subjective Memory Complaint Label Assignment
- 3.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)Google Scholar
- 5.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)Google Scholar
- 9.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)Google Scholar
- 12.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)CrossRefGoogle Scholar