N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semisupervised deep learning. Issue 1 (19th October 2020)

• Record Type: Journal Article
• Authors: Qiu, Bin; You, Yunfei; Huang, Zhiyu; Meng, Xiangxi; Jiang, Zhe; Zhou, Chuanqing; Liu, Gangjun; Yang, Kun; Ren, Qiushi; Lu, Yanye
• Is Part Of: Journal of biophotonics. Volume 14:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-19
• Subjects: denoising -- optical coherence tomography -- semisupervised deep learning -- super‐resolution; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202000282
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal‐to‐noise ratio (SNR) and high‐resolution (HR) OCT images within a short scanning time, we presented a learning‐based method to recover high‐quality OCT images from noisy and low‐resolution OCT images. We proposed a semisupervised learning approach named N2NSR‐OCT, to generate denoised and super‐resolved OCT images simultaneously using up‐ and down‐sampling networks (U‐Net (Semi) and DBPN (Semi)). Additionally, two different super‐resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high‐quality OCT image of the corresponding down‐sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up‐ and down‐sampling networks, and can produce better performance than other related state‐of‐the‐art methods in the aspects of maintaining subtle fine retinal structures. Abstract : Optical coherence tomography (OCT) is an instrument widely used in the clinics, to diagnose eye and skin diseases. However, this examination is somewhat time‐consuming. In this paper, we proposed a method to use easy‐to‐get data to train a deep learning model, which could obtain high quality OCT images in a shorter examination time. The highlight of this method is that no high‐quality image is needed to train the network.