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Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images |
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| Authors: | Bin Qiu Shuang Zeng Xiangxi Meng Zhe Jiang Yunfei You Mufeng Geng Ziyuan Li Yicheng Hu Zhiyu Huang Chuanqing Zhou Qiushi Ren Yanye Lu |
| Institution: | 1. Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China;2. Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China;3. Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China;4. Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China;5. Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China;6. Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China |
| Abstract: | As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice. In this article, we conducted a comparative study to investigate the denoising performance of OCT images over different deep neural networks through an unsupervised Noise2Noise (N2N) strategy, which only trained with noisy OCT samples. Four representative network architectures including U-shaped model, multi-information stream model, straight-information stream model and GAN-based model were investigated on an OCT image dataset acquired from healthy human eyes. The results demonstrated all four unsupervised N2N models offered denoised OCT images with a performance comparable with that of supervised learning models, illustrating the effectiveness of unsupervised N2N models in denoising OCT images. Furthermore, U-shaped models and GAN-based models using UNet network as generator are two preferred and suitable architectures for reducing speckle noise of OCT images and preserving fine structure information of retinal layers under unsupervised N2N circumstances. |
| Keywords: | noise reduction Noise2Noise strategy optical coherence tomography images unsupervised learning |