Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function
Bin Qiu, Zhiyu Huang, Xi Liu, Xiangxi Meng, Yunfei You, Gangjun Liu, Kun Yang, Andreas Maier, Qiushi Ren, Yanye Lu
Abstract
Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers' eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception.