Co-Unet-GAN: a co-learning domain adaptation model on echocardiography segmentation

Junyang Cai; Christopher M. Haggerty; Joshua V. Stough

doi:10.1117/12.2652621

3 April 2023 Co-Unet-GAN: a co-learning domain adaptation model on echocardiography segmentation

Junyang Cai, Christopher M. Haggerty, Joshua V. Stough

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641B (2023) https://doi.org/10.1117/12.2652621
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Convolutional neural networks (CNN) are a powerful deep learning method for medical image segmentation. However performance in clinical practice often deteriorates when attempting to generalize models trained from a particular source domain to a different target domain (e.g. different vendor, acquisition parameters, protocols). To address this issue, domain adaptation has attracted increasing attention because it can minimize distribution differences among different but related domains. Extending from this prior work, we introduce Co-Unet-GAN, a co-learning domain adaptation and segmentation model addressing the domain shift problem. In this model, we train a Unet segmentation network and an image translation generative adversarial network (GAN) together to generalize performance across domains given supervised data only in the source domain. We evaluate our model on two large open echocardiography datasets, using the CAMUS set as supervised source domain and EchoNet-Dynamic as the unsupervised target. In this context, we obtain mean absolute error on ejection fraction of 9.67% on Co-Unet-GAN compared to 11.28% for a previously published Unet-GAN. Our Co-Unet-GAN for image translation and segmentation is a promising solution to the domain shift problem.

Conference Presentation

Citation Download Citation

Junyang Cai, Christopher M. Haggerty, and Joshua V. Stough "Co-Unet-GAN: a co-learning domain adaptation model on echocardiography segmentation", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641B (3 April 2023); https://doi.org/10.1117/12.2652621

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