Learning-based heart coverage estimation for short-axis cine cardiac MR images

Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Ben Glocker, Antonio de Marvao, Declan O’Regan, Stuart Cook, Daniel Rueckert

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

3 Citations (Scopus)

Abstract

The correct acquisition of short axis (SA) cine cardiac MR image stacks requires the imaging of the full cardiac anatomy between the apex and the mitral valve plane via multiple 2D slices. While in the clinical practice the SA stacks are usually checked qualitatively to ensure full heart coverage, visual inspection can become infeasible for large amounts of imaging data that is routinely acquired, e.g. in population studies such as the UK Biobank (UKBB). Accordingly, we propose a learning-based technique for the fully-automated estimation of the heart coverage for SA image stacks. The technique relies on the identification of cardiac landmarks (i.e. the apex and the mitral valve sides) on two chamber view long axis images and on the comparison of the landmarks’ positions to the volume covered by the SA stack. Landmark detection is performed using a hybrid random forest approach integrating both regression and structured classification models. The technique was applied on 3000 cases from the UKBB and compared to visual assessment. The obtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicate that the proposed technique is able to correctly detect the vast majority of the cases with insufficient coverage, suggesting that it could be used as a fully-automated quality control step for CMR SA image stacks.

Original languageEnglish
Title of host publicationFunctional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
EditorsMihaela Pop, Graham A. Wright
PublisherSpringer Verlag
Pages73-82
Number of pages10
ISBN (Print)9783319594477
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada
Duration: 2017 Jun 112017 Jun 13

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

Conference

Conference9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017
Country/TerritoryCanada
CityToronto
Period17/6/1117/6/13

Keywords

  • Cardiac MR
  • Heart coverage
  • Landmark detection
  • Quality control

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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