A semi-supervised large margin algorithm for white matter hyperintensity segmentation
Qin C, Moreno RG, Bowles C, Ledig C, Scheltens P, Barkhof F, Rhodius-Meester H, Tijms B, Lemstra AW, Van Der Flier WM, Glocker B, Rueckert D (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 10019 LNCS
Pages Range: 104-112
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Athens, GRC
ISBN: 9783319471563
DOI: 10.1007/978-3-319-47157-0_13
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.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Vrije Universiteit Amsterdam (VU) / University Amsterdam
Netherlands (NL)
United Kingdom (GB)
Vrije Universiteit Amsterdam (VU) / University Amsterdam
Netherlands (NL)
How to cite
APA:
Qin, C., Moreno, R.G., Bowles, C., Ledig, C., Scheltens, P., Barkhof, F.,... Rueckert, D. (2016). A semi-supervised large margin algorithm for white matter hyperintensity segmentation. In Li Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 104-112). Athens, GRC: Springer Verlag.
MLA:
Qin, Chen, et al. "A semi-supervised large margin algorithm for white matter hyperintensity segmentation." Proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, GRC Ed. Li Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang, Springer Verlag, 2016. 104-112.
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