Azarmehr, N. orcid.org/0000-0002-6367-207X, Ye, X., Howard, J.P. et al. (7 more authors) (2021) Neural architecture search of echocardiography view classifiers. Journal of Medical Imaging, 8 (3). 034002. ISSN 2329-4302
Abstract
Purpose: Echocardiography is the most commonly used modality for assessing the heart in clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from different orientations and positions, thereby creating different viewpoints for assessing the cardiac function. The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis.
Approach: In this study, convolutional neural networks are used for the automated identification of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset of 8732 videos acquired from 374 patients. Differentiable architecture search approach was utilized to design small neural network architectures for rapid inference while maintaining high accuracy. The impact of the image quality and resolution, size of the training dataset, and number of echocardiographic view classes on the efficacy of the models were also investigated.
Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1% to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms.
Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require less training data. Such models can be used for real-time detection of the standard views.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | deep learning; echocardiography; neural architecture search; view classification; AutoML |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Aug 2021 14:45 |
Last Modified: | 19 Aug 2021 09:26 |
Status: | Published |
Publisher: | SPIE-Intl Soc Optical Eng |
Refereed: | Yes |
Identification Number: | 10.1117/1.jmi.8.3.034002 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177141 |