King's College London

Research portal

Regional Multi-view Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients

Research output: Contribution to journalArticlepeer-review

Esther Puyol, Bram Ruijsink, Bernhard Gerber, Mihaela Silvia Amzulescu, Helene Langet, Mathieu De Craene, Julia A. Schnabel, Paolo Pior, Andrew P. King

Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
Accepted/In press11 Aug 2018
Published15 Aug 2018


  • Regional Multi-view Learning for_ANTON_Accepted11August2018_GREEN AAM

    TBME_18.pdf, 1.79 MB, application/pdf

    Uploaded date:13 Aug 2018

    Version:Accepted author manuscript

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted.

King's Authors


Objective: The aim of this paper is to describe an automated diagnostic pipeline which uses as input only ultrasound (US) data, but is at the same time informed by a training database of multimodal magnetic resonance (MR) and US image data.
Methods: We create a multimodal cardiac motion atlas from 3D MR and 3D US data followed by multi-view machine learning algorithms to combine and extract the most meaningful cardiac descriptors for classification of dilated cardiomyopathy (DCM) patients using US data only. More specifically, we propose two algorithms based on: multi-view linear discriminant analysis (MLDA) and multi-view Laplacian support vector machines (MvLapSVM). Furthermore, a novel regional multi-view approach is proposed to exploit the regional relationships between the two modalities.
Results: We evaluate our pipeline on the classification task of discriminating between normals and DCM patients. Results show that the use of multi-view classifiers together with a cardiac motion atlas results in a statistically significant improvement in accuracy compared to classification without the multimodal atlas. MvLapSVM was able to achieve the highest accuracy for both the global
approach (92.71%) and the regional approach (94.32%).
Conclusion: Our work represents an important contribution to the understanding
of cardiac motion, which is an important aid in the quantification of the contractility and function of the left ventricular myocardium.
Significance: The intended workflow of the developed pipeline is to make use of the prior knowledge from the multimodal atlas to enable robust extraction of indicators from 3D US images for detecting DCM patients.

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454