ArticleDiagnostics (Basel, Switzerland)2022
AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Synthesize contrast-enhanced ultrasound image of thyroid nodules via generative adversarial networks.European radiology · 2026Article
- Research on concept generation design of automobile seats based on human-machine co-creation.Scientific reports · 2025Article
- TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis.Frontiers in bioengineering and biotechnology · 2024Article
- Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound.Frontiers in oncology · 2023Review
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Authors and funding
7 authors at 2 institutions in 1 country.
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Abstract
problemUltrasonography 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.
aimin 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.
methodsthis 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.
resultsa 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.
conclusionthe 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.
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