PuSH - Publication Server of Helmholtz Zentrum München

Öksüz, I.* ; Ruijsink, B.* ; Puyol-Antón, E.* ; Sinclair, M.* ; Rueckert, D.* ; Schnabel, J.A.* ; King, A.P.*

Automatic left ventricular outflow tract classification for accurate cardiac MR planning.

In: (2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 04-07 April 2018, Washington, DC, USA). 2018. 462-465 (Proceedings - International Symposium on Biomedical Imaging ; 2018-April)
DOI
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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Conference contribution
Corresponding Author
Keywords Cardiac Mr ; Convolutional Neural Networks ; Image Quality Assessment ; Lvot ; Uk Biobank
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Conference Title 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Conference Date 04-07 April 2018
Conference Location Washington, DC, USA
Quellenangaben Volume: 2018-April, Issue: , Pages: 462-465 Article Number: , Supplement: ,
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)