Imperial College London
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DrAntonioSimoes Monteiro de Marvao

Faculty of Medicine, Institute of Clinical Sciences

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 3313 1510antonio.de-marvao

 
 
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Location

 

Robert Steiner MRI UnitHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Tarroni:2017:10.1007/978-3-319-59448-4_8,
author = {Tarroni, G and Oktay, O and Bai, W and Schuh, A and Suzuki, H and Passerat-Palmbach, J and Glocker, B and de, Marvao A and O'Regan, D and Cook, S and Rueckert, D},
doi = {10.1007/978-3-319-59448-4_8},
pages = {73--82},
publisher = {Springer},
title = {Learning-based heart coverage estimation for short-axis cine cardiac MR images},
url = {http://dx.doi.org/10.1007/978-3-319-59448-4_8},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The correct acquisition of short axis (SA) cine cardiac MRimage stacks requires the imaging of the full cardiac anatomy betweenthe apex and the mitral valve plane via multiple 2D slices. While in theclinical practice the SA stacks are usually checked qualitatively to en-sure full heart coverage, visual inspection can become infeasible for largeamounts of imaging data that is routinely acquired, e.g. in populationstudies  such  as  the  UK  Biobank  (UKBB).  Accordingly,  we  propose  alearning-based technique for the fully-automated estimation of the heartcoverage for SA image stacks. The technique relies on the identificationof cardiac landmarks (i.e. the apex and the mitral valve sides) on twochamber view long axis images and on the comparison of the landmarks’positions to the volume covered by the SA stack. Landmark detection isperformed using a hybrid random forest approach integrating both re-gression and structured classification models. The technique was appliedon 3000 cases from the UKBB and compared to visual assessment. Theobtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicatethat the proposed technique is able to correctly detect the vast majorityof the cases with insufficient coverage, suggesting that it could be usedas a fully-automated quality control step for CMR SA image stacks.
AU  - Tarroni,G
AU  - Oktay,O
AU  - Bai,W
AU  - Schuh,A
AU  - Suzuki,H
AU  - Passerat-Palmbach,J
AU  - Glocker,B
AU  - de,Marvao A
AU  - O'Regan,D
AU  - Cook,S
AU  - Rueckert,D
DO  - 10.1007/978-3-319-59448-4_8
EP  - 82
PB  - Springer
PY  - 2017///
SP  - 73
TI  - Learning-based heart coverage estimation for short-axis cine cardiac MR images
UR  - http://dx.doi.org/10.1007/978-3-319-59448-4_8
UR  - http://hdl.handle.net/10044/1/46118
ER  -
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