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Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection

Oksuz I, Ruijsink B, Puyol-Anton E, Bustin A, Cruz G, Prieto C, Rueckert D, Schnabel JA, King AP (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11070 LNCS

Pages Range: 250-258

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030009274

DOI: 10.1007/978-3-030-00928-1_29

Abstract

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1 ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.

Involved external institutions

How to cite

APA:

Oksuz, I., Ruijsink, B., Puyol-Anton, E., Bustin, A., Cruz, G., Prieto, C.,... King, A.P. (2018). Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection. In Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 250-258). Granada, ESP: Springer Verlag.

MLA:

Oksuz, Ilkay, et al. "Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi, Springer Verlag, 2018. 250-258.

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