Kande Nilesh A, Dakhane Rupali, Dukkipati Ambedkar, Yalavarthy Phaneendra Kumar
IEEE Trans Med Imaging. 2021 Jan;40(1):180-192. doi: 10.1109/TMI.2020.3024097. Epub 2020 Dec 29.
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.
光学相干断层扫描（OCT）是评估眼科疾病的标准诊断成像方法。高速OCT图像中存在的散斑噪声妨碍了其临床应用，尤其是在谱域光学相干断层扫描（SDOCT）中。在这项工作中，开发了一种名为暹罗生成对抗网络（SiameseGAN）的新型深度生成模型，用于对SDOCT的低信噪比（LSNR）B扫描进行去噪。SiameseGAN是一种配备暹罗孪生网络的生成对抗网络（GAN）。所提出的SiameseGAN模型的暹罗网络模块有助于生成器在特征空间中生成更接近真实图像的去噪图像，而判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，这种方法在去噪时不需要来自目标成像对象的先验高质量图像，并且去噪所需时间更短。此外，这项工作还部署了各种已被证明在SDOCT成像中执行去噪任务有效的深度学习模型。已对所提出的方法与这些先进的去噪算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，使用所提出的SiameseGAN可以有效减轻散斑噪声，并且去噪速度更快。
