Automatic left ventricular outflow tract classification for accurate cardiac MR planning

Ilkay Oksuz; Bram Ruijsink; Esther Puyol-Anton; Matthew Sinclair; Daniel Rueckert; Julia A. Schnabel; Andrew P. King

doi:10.1109/ISBI.2018.8363616

Automatic left ventricular outflow tract classification for accurate cardiac MR planning

Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, Matthew Sinclair, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Cardiac MR planning is important to ensure high quality image data and to enable accurate quantification of cardiac function. One result of inaccurate planning is an 'off-axis' orientation of the 4-chamber view, often recognized by the presence of the left ventricular outflow tract (LVOT). This can lead to difficulties in assessment of atrial volumes and septal wall motion, either manually by experts or by automated image analysis algorithms. For large datasets such as the UK biobank, manual labelling is tedious and automated analysis pipelines including automatic image quality assessment need to be developed. In this paper, we propose a method to automatically detect the presence of the LVOT in cardiac MRI, which can aid identifying poorly planned 4-chamber images. Our method is based on Convolutional Neural Networks (CNNs) and is able to detect LVOT in 4-chamber images in less than 1ms. We test our algorithm on a subset of the UK biobank dataset (246 cardiac MR images) and achieve an average accuracy of 83%. We compare our approach to a range of state of the art classification methods.

Original language	English
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	462-465
Number of pages	4
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363616
Publication status	Published - 23 May 2018
Externally published	Yes
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: 4 Apr 2018 → 7 Apr 2018

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/Territory	United States
City	Washington
Period	4/04/18 → 7/04/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at School of Biomedical Engineering and Imaging Sciences, Kings College London (WT 203148/Z/16/Z). This research has been conducted using the UK Biobank Resource under Application Number 17806. ∗ Joint last authors.

Funders	Funder number
Wellcome EPSRC Centre for Medical Engineering at School of Biomedical Engineering and Imaging Sciences, Kings College London	WT 203148/Z/16/Z
Engineering and Physical Sciences Research Council	EP/P001009/1, EP/N026993/1, EP/M000133/1

Keywords

Cardiac MR
Convolutional Neural Networks
Image Quality Assessment
LVOT
UK Biobank

Access to Document

10.1109/ISBI.2018.8363616

Cite this

Oksuz, I., Ruijsink, B., Puyol-Anton, E., Sinclair, M., Rueckert, D., Schnabel, J. A., & King, A. P. (2018). Automatic left ventricular outflow tract classification for accurate cardiac MR planning. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 462-465). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363616

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abstract = "Cardiac MR planning is important to ensure high quality image data and to enable accurate quantification of cardiac function. One result of inaccurate planning is an 'off-axis' orientation of the 4-chamber view, often recognized by the presence of the left ventricular outflow tract (LVOT). This can lead to difficulties in assessment of atrial volumes and septal wall motion, either manually by experts or by automated image analysis algorithms. For large datasets such as the UK biobank, manual labelling is tedious and automated analysis pipelines including automatic image quality assessment need to be developed. In this paper, we propose a method to automatically detect the presence of the LVOT in cardiac MRI, which can aid identifying poorly planned 4-chamber images. Our method is based on Convolutional Neural Networks (CNNs) and is able to detect LVOT in 4-chamber images in less than 1ms. We test our algorithm on a subset of the UK biobank dataset (246 cardiac MR images) and achieve an average accuracy of 83%. We compare our approach to a range of state of the art classification methods.",

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Oksuz, I, Ruijsink, B, Puyol-Anton, E, Sinclair, M, Rueckert, D, Schnabel, JA & King, AP 2018, Automatic left ventricular outflow tract classification for accurate cardiac MR planning. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 462-465, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/04/18. https://doi.org/10.1109/ISBI.2018.8363616

Automatic left ventricular outflow tract classification for accurate cardiac MR planning. / Oksuz, Ilkay; Ruijsink, Bram; Puyol-Anton, Esther et al.
2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 462-465 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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ER -

Oksuz I, Ruijsink B, Puyol-Anton E, Sinclair M, Rueckert D, Schnabel JA et al. Automatic left ventricular outflow tract classification for accurate cardiac MR planning. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society. 2018. p. 462-465. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2018.8363616

Automatic left ventricular outflow tract classification for accurate cardiac MR planning

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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Cite this