Zhang, L, Pereañez, M, Piechnik, SK et al. (3 more authors) (2018) Multi-input and dataset-invariant adversarial learning (MDAL) for left and right-ventricular coverage estimation in cardiac MRI. In: Lecture Notes in Computer Science. 21st International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2018, 16-20 Sep 2018, Granada, Spain,. Springer Verlag , pp. 481-489. ISBN 9783030009335
Abstract
Cardiac functional parameters, such as, the Ejection Fraction (EF) and Cardiac Output (CO) of both ventricles, are most immediate indicators of normal/abnormal cardiac function. To compute these parameters, accurate measurement of ventricular volumes at end-diastole (ED) and end-systole (ES) are required. Accurate volume measurements depend on the correct identification of basal and apical slices in cardiac magnetic resonance (CMR) sequences that provide full coverage of both left (LV) and right (RV) ventricles. This paper proposes a novel adversarial learning (AL) approach based on convolutional neural networks (CNN) that detects and localizes the basal/apical slices in an image volume independently of image-acquisition parameters, such as, imaging device, magnetic field strength, variations in protocol execution, etc. The proposed model is trained on multiple cohorts of different provenance, and learns image features from different MRI viewing planes to learn the appearance and predict the position of the basal and apical planes. To the best of our knowledge, this is the first work tackling the fully automatic detection and position regression of basal/apical slices in CMR volumes in a dataset-invariant manner. We achieve this by maximizing the ability of a CNN to regress the position of basal/apical slices within a single dataset, while minimizing the ability of a classifier to discriminate image features between different data sources. Our results show superior performance over state-of-the-art methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2018. 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. |
Keywords: | Deep learning; Dataset invariance; Adversarial learning; Ventricular coverage assessment; MRI |
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 10:40 |
Last Modified: | 30 Apr 2019 10:40 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-00934-2_54 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145286 |