Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Biffi:2019:10.1109/ISBI.2019.8759328,
author = {Biffi, C and Cerrolaza, JJ and Tarroni, G and de, Marvao A and Cook, SA and O'Regan, DP and Rueckert, D},
doi = {10.1109/ISBI.2019.8759328},
pages = {1643--1646},
publisher = {IEEE},
title = {3D high-resolution cardiac segmentation reconstruction from 2D views using conditional variational autoencoders},
url = {http://dx.doi.org/10.1109/ISBI.2019.8759328},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 ).
AU  - Biffi,C
AU  - Cerrolaza,JJ
AU  - Tarroni,G
AU  - de,Marvao A
AU  - Cook,SA
AU  - O'Regan,DP
AU  - Rueckert,D
DO  - 10.1109/ISBI.2019.8759328
EP  - 1646
PB  - IEEE
PY  - 2019///
SN  - 1945-7928
SP  - 1643
TI  - 3D high-resolution cardiac segmentation reconstruction from 2D views using conditional variational autoencoders
UR  - http://dx.doi.org/10.1109/ISBI.2019.8759328
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000485040000350&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8759328
UR  - http://hdl.handle.net/10044/1/73720
ER  -
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