Imperial College London

ProfessorDarrelFrancis

Faculty of MedicineNational Heart & Lung Institute

Professor of Cardiology
 
 
 
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Contact

 

+44 (0)20 7594 3381d.francis Website

 
 
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Assistant

 

Miss Juliet Holmes +44 (0)20 7594 5735

 
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Location

 

Block B Hammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@inbook{Azarmehr:2020:10.1007/978-3-030-39343-4_43,
author = {Azarmehr, N and Ye, X and Sacchi, S and Howard, JP and Francis, DP and Zolgharni, M},
doi = {10.1007/978-3-030-39343-4_43},
pages = {497--504},
title = {Segmentation of Left Ventricle in 2D Echocardiography Using Deep Learning},
url = {http://dx.doi.org/10.1007/978-3-030-39343-4_43},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - CHAP
AB - © 2020, Springer Nature Switzerland AG. The segmentation of Left Ventricle (LV) is currently carried out manually by the experts, and the automation of this process has proved challenging due to the presence of speckle noise and the inherently poor quality of the ultrasound images. This study aims to evaluate the performance of different state-of-the-art Convolutional Neural Network (CNN) segmentation models to segment the LV endocardium in echocardiography images automatically. Those adopted methods include U-Net, SegNet, and fully convolutional DenseNets (FC-DenseNet). The prediction outputs of the models are used to assess the performance of the CNN models by comparing the automated results against the expert annotations (as the gold standard). Results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.93 ± 0.04, and Hausdorff distance of 4.52 ± 0.90.
AU - Azarmehr,N
AU - Ye,X
AU - Sacchi,S
AU - Howard,JP
AU - Francis,DP
AU - Zolgharni,M
DO - 10.1007/978-3-030-39343-4_43
EP - 504
PY - 2020///
SN - 9783030393427
SP - 497
TI - Segmentation of Left Ventricle in 2D Echocardiography Using Deep Learning
UR - http://dx.doi.org/10.1007/978-3-030-39343-4_43
ER -