Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Lourenço:2020,
author = {Lourenço, A and Kerfoot, E and Dibblin, C and Alskaf, E and Anjari, M and Bharath, AA and King, AP and Chubb, H and Correia, TM and Varela, M},
publisher = {arXiv},
title = {Left atrial ejection fraction estimation using SEGANet for fully  automated segmentation of CINE MRI},
url = {http://arxiv.org/abs/2008.13718v1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - 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 toautomatically characterise atrial function and aid in treatment selection forAF. Clinical metrics of left atrial (LA) function, such as ejection fraction(EF) and active atrial contraction ejection fraction (aEF), are promising, buthave until now typically relied on volume estimations extrapolated fromsingle-slice images. In this work, we study volumetric functional biomarkers ofthe LA using a fully automatic SEGmentation of the left Atrium based on aconvolutional neural Network (SEGANet). SEGANet was trained using a dedicateddata augmentation scheme to segment the LA, across all cardiac phases, in shortaxis dynamic (CINE) Magnetic Resonance Images (MRI) acquired with full cardiaccoverage. Using the automatic segmentations, we plotted volumetric time curvesfor the LA and estimated LA EF and aEF automatically. The proposed methodyields high quality segmentations that compare well with manual segmentations(Dice scores [$0.93 \pm 0.04$], median contour [$0.75 \pm 0.31$] mm andHausdorff distances [$4.59 \pm 2.06$] mm). LA EF and aEF are also in agreementwith literature values and are significantly higher in AF patients than inhealthy volunteers. Our work opens up the possibility of automaticallyestimating LA volumes and functional biomarkers from multi-slice CINE MRI,bypassing the limitations of current single-slice methods and improving thecharacterisation of atrial function in AF patients.
AU  - Lourenço,A
AU  - Kerfoot,E
AU  - Dibblin,C
AU  - Alskaf,E
AU  - Anjari,M
AU  - Bharath,AA
AU  - King,AP
AU  - Chubb,H
AU  - Correia,TM
AU  - Varela,M
PB  - arXiv
PY  - 2020///
TI  - Left atrial ejection fraction estimation using SEGANet for fully  automated segmentation of CINE MRI
UR  - http://arxiv.org/abs/2008.13718v1
UR  - http://hdl.handle.net/10044/1/83441
ER  -
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