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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Classification ; Multimodal Cardiac Motion Atlas ; Multiview Dimensionality Reduction
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
International Workshop on Statistical Atlases and Computational Models of the Heart
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 10663 LNCS,
Seiten: 3-11
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)