Optical coherence tomography image denoising using a generative adversarial network with speckle modulation.,Journal of Biophotonics

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Optical coherence tomography image denoising using a generative adversarial network with speckle modulation.
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2020-02-03 , DOI: 10.1002/jbio.201960135
Zhao Dong _{1,

2} , Guoyan Liu _{3,

4} , Guangming Ni ₂ , Jason Jerwick _{1,

2} , Lian Duan ₁ , Chao Zhou _{1,

2,

4}

Affiliation

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle‐modulating OCT (SM‐OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM‐OCT images were used for training and validating the neural network model, which we call SM‐GAN. The performance of the SM‐GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM‐GAN model was compared to traditional OCT denoising methods and other state‐of‐the‐art deep learning based denoise networks. We conclude that the SM‐GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.

中文翻译：

使用具有散斑调制的生成对抗网络进行光学相干断层扫描图像去噪。

光学相干断层扫描（OCT）广泛应用于生物医学成像和临床诊断。然而，散斑噪声是影响OCT图像质量的关键因素。在这里，我们开发了一个定制的生成对抗网络（GAN）来对 OCT 图像进行去噪。构建散斑调制 OCT (SM-OCT) 来生成用作地面实况的低散斑图像。总共使用了 210,000 张 SM-OCT 图像来训练和验证神经网络模型，我们将其称为 SM-GAN。使用在线基准视网膜图像、从人类手指获取的 3D OCT 图像以及跳动的果蝇心脏的 OCT 视频进一步证明了 SM-GAN 方法的性能。将 SM-GAN 模型的去噪性能与传统 OCT 去噪方法和其他最先进的基于深度学习的去噪网络进行了比较。我们得出的结论是，这里提出的 SM-GAN 模型可以有效减少 OCT 图像和视频中的散斑噪声，同时保持空间和时间分辨率。

更新日期：2020-02-03

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