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.
Highlights
The superficial location of the thyroid gland makes high-resolution ultrasound the imaging modality of choice for the evaluation of diffuse disease and nodules
We compared the visual performance of the elastography image generated by our model with that of three networks
This subsection compared the automated generation method of ultrasound elastography (AUE-net) with the I2IT methods described above using three image similarity metrics (PSNR, structural similarity index measure (SSIM) and FID) and scores from the specialists
Summary
The superficial location of the thyroid gland makes high-resolution ultrasound the imaging modality of choice for the evaluation of diffuse disease and nodules. Patients may be referred for ultrasonography because of palpable abnormalities. Detection of nonpalpable nodules can be as high as 70% and varies by imaging modality [1]. The prevalence of thyroid nodules is high and they may be neoplastic. Most of nodules are benign proliferative structural changes and benign follicular adenomas, with only a small percentage of malignancy [2]. Further confirmation of malignancy for thyroid nodulemainly relies on Fine
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