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

TY - JOUR

T1 - Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection

AU - Oksuz, Ilkay

AU - Ruijsink, Bram

AU - Puyol-Antón, Esther

AU - Bustin, Aurelien

AU - Cruz, Gastao

AU - Prieto, Claudia

AU - Rueckert, Daniel

AU - Schnabel, Julia A.

AU - King, Andrew P.

PY - 2018/9/26

Y1 - 2018/9/26

N2 - 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.

AB - 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.

KW - Cardiac MR

KW - Convolutional Neural Networks

KW - Image quality assessment

KW - Motion artefacts

KW - UK Biobank

UR - http://www.scopus.com/inward/record.url?scp=85054097617&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-00928-1_29

DO - 10.1007/978-3-030-00928-1_29

M3 - Article

AN - SCOPUS:85054097617

SN - 0302-9743

SP - 250

EP - 258

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

Y2 - 16 September 2018 through 20 September 2018

ER -