Imperial College London
Alternate

Professor Paul M. Matthews

Faculty of Medicine, Department of Brain Sciences

Edmond and Lily Safra Chair, Head of Department
 
 
 
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Contact

 

+44 (0)20 7594 2855p.matthews

 
 
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Assistant

 

Ms Siobhan Dillon +44 (0)20 7594 2855

 
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Location

 

E502Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bai:2017,
author = {Bai, W and Sinclair, M and Tarroni, G and Oktay, O and Rajchl, M and Vaillant, G and Lee, AM and Aung, N and Lukaschuk, E and Sanghvi, MM and Zemrak, F and Fung, K and Paiva, JM and Carapella, V and Kim, YJ and Suzuki, H and Kainz, B and Matthews, PM and Petersen, SE and Piechnik, SK and Neubauer, S and Glocker, B and Rueckert, D},
title = {Automated cardiovascular magnetic resonance image analysis with fully  convolutional networks},
url = {http://arxiv.org/abs/1710.09289v4},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Cardiovascular magnetic resonance (CMR) imaging is a standard imagingmodality for assessing cardiovascular diseases (CVDs), the leading cause ofdeath globally. CMR enables accurate quantification of the cardiac chambervolume, ejection fraction and myocardial mass, providing information fordiagnosis and monitoring of CVDs. However, for years, clinicians have beenrelying on manual approaches for CMR image analysis, which is time consumingand prone to subjective errors. It is a major clinical challenge toautomatically derive quantitative and clinically relevant information from CMRimages. Deep neural networks have shown a great potential in image patternrecognition and segmentation for a variety of tasks. Here we demonstrate anautomated analysis method for CMR images, which is based on a fullyconvolutional network (FCN). The network is trained and evaluated on alarge-scale dataset from the UK Biobank, consisting of 4,875 subjects with93,500 pixelwise annotated images. The performance of the method has beenevaluated using a number of technical metrics, including the Dice metric, meancontour distance and Hausdorff distance, as well as clinically relevantmeasures, including left ventricle (LV) end-diastolic volume (LVEDV) andend-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolicvolume (RVEDV) and end-systolic volume (RVESV). By combining FCN with alarge-scale annotated dataset, the proposed automated method achieves a highperformance on par with human experts in segmenting the LV and RV on short-axisCMR images and the left atrium (LA) and right atrium (RA) on long-axis CMRimages.
AU  - Bai,W
AU  - Sinclair,M
AU  - Tarroni,G
AU  - Oktay,O
AU  - Rajchl,M
AU  - Vaillant,G
AU  - Lee,AM
AU  - Aung,N
AU  - Lukaschuk,E
AU  - Sanghvi,MM
AU  - Zemrak,F
AU  - Fung,K
AU  - Paiva,JM
AU  - Carapella,V
AU  - Kim,YJ
AU  - Suzuki,H
AU  - Kainz,B
AU  - Matthews,PM
AU  - Petersen,SE
AU  - Piechnik,SK
AU  - Neubauer,S
AU  - Glocker,B
AU  - Rueckert,D
PY  - 2017///
TI  - Automated cardiovascular magnetic resonance image analysis with fully  convolutional networks
UR  - http://arxiv.org/abs/1710.09289v4
UR  - http://hdl.handle.net/10044/1/60254
ER  -
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