Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning.

ABSTRACT: Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion 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) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real cli

SUBMITTER: Oksuz I 

PROVIDER: S-EPMC6688894 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature