Abstract
2D echocardiography (echo) is the most widely used imaging technique to identify cardiac disease. In addition to anatomical variability in patients, the quality of acquired echo image can vary significantly depending on the ultrasound (US) machine and the experience level of the operator, where a poor image quality can affect the diagnosis. This variability can also result in reduced performance of machine learning models trained on these data. With the recent advances in generative adversarial networks (GAN), we demonstrate that it is possible to transfer the image quality of echo images to a user-defined quality level with the use of a multi-domain transfer approach referred as StarGAN. The proposed quality transfer StarGAN (QT-StarGAN) requires no pairs of low-and high-quality echo images and incorporates the temporal information of echo images during the training phase. We evaluate the proposed approach using 16,612 echo cine series obtained from 3,157 patients. Using a standard echo view classification task, we demonstrate that the accuracy of classification is significantly improved using QT-StarGAN.
Z. Liao, M. H. Jafari and H. Girgis–Joint first authorship.
P. Abolmaesumi and T. Tsang–Joint senior authorship.
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Notes
- 1.
14 standard cardiac views: see the 13 class labels in Fig. 5(b) and due to the extreme similarity, PSAX-{M, PM} views are combined as one class. This dataset was collected from Vancouver General Hospital PACS system, under the approvals from the institutional Medical Research Ethics Board and the Information Privacy Office.
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Acknowledgements
This work is supported in part by the Canadian Institutes of Health Research (CIHR) and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to acknowledge the support provided by Dale Hawley and the Vancouver Coastal Health in providing us with the anonymized, de-identified data.
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Liao, Z. et al. (2019). Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_76
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