Feature tracking cardiac magnetic resonance via deep learning and spline optimization

Davis M. Vigneault, Weidi Xie, David A. Bluemke, J. Alison Noble

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ∼ 97%, intersection-over-union (IoU) across all classes was ∼ 87%, and IoU across foreground classes only was ∼ 85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (−25.3% vs −29.1%, p = 0.006), in agreement with the current clinical literature.

Original languageEnglish (US)
Title of host publicationFunctional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
EditorsMihaela Pop, Graham A. Wright
PublisherSpringer Verlag
Pages183-194
Number of pages12
ISBN (Print)9783319594477
DOIs
StatePublished - 2017
Event9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada
Duration: Jun 11 2017Jun 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017
CountryCanada
CityToronto
Period6/11/176/13/17

Keywords

  • Cardiac magnetic resonance
  • Deep convolutional neural networks
  • Least squares optimization
  • Quadratic basis splines
  • Regional cardiac function

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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