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Öksüz, I.* ; Ruijsink, B.* ; Puyol-Antón, E.* ; Bustin, A.* ; Cruz, G.* ; Prieto, C.* ; Rueckert, D.* ; Schnabel, J.A.* ; King, A.P.*

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

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2018. 250-258 (Lect. Notes Comput. Sc. ; 11070 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Cardiac Mr ; Convolutional Neural Networks ; Image Quality Assessment ; Motion Artefacts ; Uk Biobank
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Medical Image Computing and Computer-Assisted Intervention
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11070 LNCS, Heft: , Seiten: 250-258 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)