AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network

Qingjie Zhang; Junjuan Zhao; Xiangmeng Long; Quanyong Luo; Ren Wang; Xuehai Ding; Chentian Shen

doi:10.3390/diagnostics12020253

AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network

Diagnostics (Basel). 2022 Jan 20;12(2):253. doi: 10.3390/diagnostics12020253.

Authors

Qingjie Zhang¹, Junjuan Zhao¹, Xiangmeng Long¹, Quanyong Luo², Ren Wang³, Xuehai Ding¹, Chentian Shen²

Affiliations

¹ School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
² Department of Nuclear Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.
³ Department of Ultrasound Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.

Abstract

Problem: Ultrasonography is recommended as the first choice for evaluation of thyroid nodules, however, conventional ultrasound features may not be able to adequately predict malignancy. Ultrasound elastography, adjunct to conventional B-mode ultrasound, can effectively improve the diagnostic accuracy of thyroid nodules. However, this technology requires professional elastography equipment and experienced physicians.

Aim: in the field of computational medicine, Generative Adversarial Networks (GANs) were proven to be a powerful tool for generating high-quality images. This work therefore utilizes GANs to generate ultrasound elastography images.

Methods: this paper proposes a new automated generation method of ultrasound elastography (AUE-net) to generate elastography images from conventional ultrasound images. The AUE-net was based on the U-Net architecture and optimized by attention modules and feature residual blocks, which could improve the adaptability of feature extraction for nodules of different sizes. The additional color loss function was used to balance color distribution. In this network, we first attempted to extract the tissue features of the ultrasound image in the latent space, then converted the attributes by modeling the strain, and finally reconstructed them into the corresponding elastography image.

Results: a total of 726 thyroid ultrasound elastography images with corresponding conventional images from 397 patients were obtained between 2019 and 2021 as the dataset (646 in training set and 80 in testing set). The mean rating accuracy of the AUE-net generated elastography images by ultrasound specialists was 84.38%. Compared with that of the existing models in the visual aspect, the presented model generated relatively higher quality elastography images.

Conclusion: the AUE-net generated ultrasound elastography images showed natural appearance and retained tissue information. Accordingly, it seems that B-mode ultrasound harbors information that can link to tissue elasticity. This study may pave the way to generate ultrasound elastography images readily without the need for professional equipment.

Keywords: attention module; color loss; generative adversarial networks; neural networks; ultrasound elastography.

Abstract

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