Pattern visualization and recognition using tensor factorization for early differential diagnosis of parkinsonism

Li R, Wu P, Yakushev I, Wang J, Ziegler SI, Förster S, Huang SC, Schwaiger M, Navab N, Zuo C, Shi K (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10435 LNCS

Pages Range: 125-133

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319661780

DOI: 10.1007/978-3-319-66179-7_15

Abstract

Idiopathic Parkinsons disease (PD) and atypical parkinsonian syndromes may have similar symptoms at the early disease stage. Pattern recognition on metabolic imaging has been confirmed of distinct value in the early differential diagnosis of Parkinsonism. However, the principal component analysis (PCA) based method ends up with a unique probability score of each disease pattern. This restricts the exploration of heterogeneous characteristic features for differentiation. There is no visualization of the underlying mechanism to assist the radiologist/neurologist either. We propose a tensor factorization based method to extract the characteristic patterns of the diseases. By decomposing the 3D data, we can capture the intrinsic characteristic pattern in the data. In particular, the disease-related patterns can be visualized individually for the inspection by physicians. The test on PET images of 206 early parkinsonian patients has confirmed differential patterns on the visualized feature images using the proposed method. Computer-aided diagnosis based on multi-class support vector machine (SVM) shown improved diagnostic accuracy of Parkinsonism using the tensor-factorized feature images compared to the state-of-the-art PCA-based scores [Tang et al. Lancet Neurol. 2010].

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Huashan Hospital CN China (CN) University of California Los Angeles (UCLA) US United States (USA) (US)

How to cite

APA:

Li, R., Wu, P., Yakushev, I., Wang, J., Ziegler, S.I., Förster, S.,... Shi, K. (2017). Pattern visualization and recognition using tensor factorization for early differential diagnosis of parkinsonism. In Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 125-133). Quebec City, QC, CAN: Springer Verlag.

MLA:

Li, Rui, et al. "Pattern visualization and recognition using tensor factorization for early differential diagnosis of parkinsonism." Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins, Springer Verlag, 2017. 125-133.

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