Zhang, L, Gooya, A, Pereanez, M et al. (5 more authors) (2019) Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging With Fisher-Discriminative 3-D CNN. IEEE transactions on bio-medical engineering, 66 (7). pp. 1975-1986. ISSN 0018-9294
Abstract
Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and is necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2-D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features, and enhances the discriminative capacity of the baseline 2-D CNN learning framework, thus, achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3-D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/. |
Keywords: | 3D convolutional neural network; LV coverage; image-quality assessment; population image analysis; Fisher discriminant criterion |
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) |
Funding Information: | Funder Grant number EPSRC EP/N026993/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Dec 2018 10:01 |
Last Modified: | 25 Jun 2023 21:38 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/tbme.2018.2881952 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139771 |