Left Atrial Ejection Fraction Estimation Using SEGANet for Fully Automated Segmentation of CINE MRI

Ana Lourenço*, Eric Kerfoot, Connor Dibblin, Ebraham Alskaf, Mustafa Anjari, Anil A. Bharath, Andrew P. King, Henry Chubb, Teresa M. Correia, Marta Varela

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

7 Citations (Scopus)

Abstract

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterised by a rapid and irregular electrical activation of the atria. Treatments for AF are often ineffective and few atrial biomarkers exist to automatically characterise atrial function and aid in treatment selection for AF. Clinical metrics of left atrial (LA) function, such as ejection fraction (EF) and active atrial contraction ejection fraction (aEF), are promising, but have until now typically relied on volume estimations extrapolated from single-slice images. In this work, we study volumetric functional biomarkers of the LA using a fully automatic SEGmentation of the left Atrium based on a convolutional neural Network (SEGANet). SEGANet was trained using a dedicated data augmentation scheme to segment the LA, across all cardiac phases, in short axis dynamic (CINE) Magnetic Resonance Images (MRI) acquired with full cardiac coverage. Using the automatic segmentations, we plotted volumetric time curves for the LA and estimated LA EF and aEF automatically. The proposed method yields high quality segmentations that compare well with manual segmentations (Dice scores [ 0.93 ± 0.04 ], median contour [ 0.75 ± 0.31 ] mm and Hausdorff distances [ 4.59 ± 2.06 ] mm). LA EF and aEF are also in agreement with literature values and are significantly higher in AF patients than in healthy volunteers. Our work opens up the possibility of automatically estimating LA volumes and functional biomarkers from multi-slice CINE MRI, bypassing the limitations of current single-slice methods and improving the characterisation of atrial function in AF patients.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
EditorsEsther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
PublisherSpringer Science and Business Media Deutschland GmbH
Pages137-145
Number of pages9
ISBN (Print)9783030681067
DOIs
Publication statusPublished - 2021
Event11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12592 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/20204/10/2020

Keywords

  • Atrial fibrillation
  • CINE MRI
  • Convolutional neural network
  • Ejection fraction
  • Left atrium
  • Segmentation

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