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Open Access Green as soon as Postprint is submitted to ZB.
Multiview machine learning using an atlas of cardiac cycle motion.
In: (International Workshop on Statistical Atlases and Computational Models of the Heart). Berlin [u.a.]: Springer, 2018. 3-11 (Lect. Notes Comput. Sc. ; 10663 LNCS)
A cardiac motion atlas provides a space of reference in which the cardiac motion fields of a cohort of subjects can be directly compared. From such atlases, descriptors can be learned for subsequent diagnosis and characterization of disease. Traditionally, such atlases have been formed from imaging data acquired using a single modality. In this work we propose a framework for building a multimodal cardiac motion atlas from MR and ultrasound data and incorporate a multiview classifier to exploit the complementary information provided by the two modalities. We demonstrate that our novel framework is able to detect non ischemic dilated cardiomyopathy patients from ultrasound data alone, whilst still exploiting the MR based information from the multimodal atlas. We evaluate two different approaches based on multiview learning to implement the classifier and achieve an improvement in classification performance from 77.5% to 83.50% compared to the use of US data without the multimodal atlas.
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Publication type
Article: Conference contribution
Keywords
Classification ; Multimodal Cardiac Motion Atlas ; Multiview Dimensionality Reduction
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
International Workshop on Statistical Atlases and Computational Models of the Heart
Quellenangaben
Volume: 10663 LNCS,
Pages: 3-11
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)