Zhang, L, Gooya, A and Frangi, AF orcid.org/0000-0002-2675-528X (2017) Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets. In: Lecture Notes in Computer Science. Second International Workshop on Simulation and Synthesis in Medical Imaging: SASHIMI 2017, 10 Sep 2017, Quebec, Canada. Springer Verlag , pp. 61-68. ISBN 9783319681269
Abstract
Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Ensuring full coverage of the Left Ventricle (LV) is a basic criteria of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this paper, we propose a novel semi-supervised method to check the coverage of LV from CMR images by using generative adversarial networks (GAN), we call it Semi-Coupled-GANs (SCGANs). To identify missing basal and apical slices in a CMR volume, a two-stage framework is proposed. First, the SCGANs generate adversarial examples and extract high-level features from the CMR images; then these image attributes are used to detect missing basal and apical slices. We constructed extensive experiments to validate the proposed method on UK Biobank with more than 6000 independent volumetric MR scans, which achieved high accuracy and robust results for missing slice detection, comparable with those of state of the art deep learning methods. The proposed method, in principle, can be adapted to other CMR image data for LV coverage assessment.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Apr 2019 10:49 |
Last Modified: | 30 Apr 2019 10:49 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-319-68127-6_7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145287 |