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SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images.
IEEE Transactions on Medical Imaging ( IF 8.9 ) Pub Date : 2020-09-14 , DOI: 10.1109/tmi.2020.3024097
Nilesh A. Kande , Rupali Dakhane , Ambedkar Dukkipati , Phaneendra Kumar Yalavarthy

Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer to groundtruth images in the feature space, while the discriminator helps in making sure they are realistic images. This approach, unlike baseline dictionary learning technique (MSBTD), does not require an apriori high-quality image from the target imaging subject for denoising and takes less time for denoising. Moreover, various deep learning models that have been shown to be effective in performing denoising task in the SDOCT imaging were also deployed in this work. A qualitative and quantitative comparison on the performance of proposed method with these state-of-the-art denoising algorithms has been performed. The experimental results show that the speckle noise can be effectively mitigated using the proposed SiameseGAN along with faster denoising unlike existing approaches.

中文翻译:

SiameseGAN:用于光谱域光学相干断层扫描图像降噪的生成模型。

光学相干断层扫描(OCT)是用于评估眼科疾病的标准诊断成像方法。高速OCT图像中出现的斑点噪声妨碍了其临床实用性,尤其是在光谱域光学相干断层扫描(SDOCT)中。在这项工作中,开发了一种新的深度生成模型,称为SiameseGAN,用于对SDOCT的低信噪比(LSNR)B扫描进行消噪。SiameseGAN是配备暹罗双胞胎网络的生殖对抗网络(GAN)。提出的SiameseGAN模型的暹罗网络模块可帮助生成器生成更接近特征空间中地面图像的去噪图像,而鉴别器则有助于确保它们是逼真的图像。与基线字典学习技术(MSBTD)不同,这种方法 不需要来自目标成像对象的先验高质量图像进行降噪,并且花费更少的时间进行降噪。此外,在这项工作中还部署了各种深度学习模型,这些模型已被证明可有效执行SDOCT成像中的降噪任务。使用这些最新的去噪算法,对所提方法的性能进行了定性和定量比较。实验结果表明,与现有方法不同,使用拟议的SiameseGAN可以有效地减轻斑点噪声,并具有更快的降噪效果。使用这些最新的去噪算法,对所提方法的性能进行了定性和定量比较。实验结果表明,与现有方法不同,使用拟议的SiameseGAN可以有效地减轻斑点噪声,并具有更快的降噪效果。使用这些最新的降噪算法,对所提方法的性能进行了定性和定量比较。实验结果表明,与现有方法不同,使用建议的SiameseGAN可以有效地减轻斑点噪声,并具有更快的降噪效果。
更新日期:2020-09-14
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