Multiview machine learning using an atlas of cardiac cycle motion

Esther Puyol-Antón*, Matthew Sinclair, Bernhard Gerber, Mihaela Silvia Amzulescu, Hélène Langet, Mathieu De Craene, Paul Aljabar, Julia A. Schnabel, Paolo Piro, Andrew P. King

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
197 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)3-11
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10663 LNCS
Early online date15 Mar 2018
DOIs
Publication statusE-pub ahead of print - 15 Mar 2018
Event8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017 - Quebec City, Canada
Duration: 10 Sept 201714 Sept 2017

Keywords

  • Classification
  • Multimodal cardiac motion atlas
  • Multiview dimensionality reduction

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