SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images
SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images
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DOI：
10.1109/tmi.2020.3024097
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发表时间：
2021-01-01
影响因子：
10.6
通讯作者：
Yalavarthy, Phaneendra Kumar
中科院分区：
文献类型：
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作者：
Kande, Nilesh A.;Dakhane, Rupali;Yalavarthy, Phaneendra Kumar
光学相干断层扫描(OCT)是一种评估眼科疾病的标准诊断成像方法。高速OCT图像中存在的斑点噪声阻碍了其临床应用，特别是在频域光学相干层析成像(SDOCT)中。在这项工作中，一个新的深度生成模型，称为SiameseGAN，被称为SiameseGAN，用于去噪SDOCT的低信噪比B超。SiameseGAN是一个生成性对抗性网络(GAN)，配备了暹罗孪生网络。所提出的SiameseGAN模型的暹罗网络模块帮助生成器生成更接近特征空间中真实图像的去噪图像，而鉴别器则帮助确保它们是真实的图像。与基线字典学习技术(MSBTD)不同，该方法不需要目标成像对象的先验高质量图像来进行去噪，并且去噪所需的时间更短。此外，各种已被证明在SDOCT成像中执行去噪任务的深度学习模型也被用于这项工作。定性和定量地比较了所提出的方法与这些最先进的去噪算法的性能。实验结果表明，与现有的去噪方法相比，该算法能有效地抑制相干斑噪声，并具有更快的去噪速度。
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.