DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images. (4th September 2019)

• Record Type: Journal Article
• Authors: Shi, Fei; Cai, Ning; Gu, Yunbo; Hu, Dianlin; Ma, Yuhui; Chen, Yang; Chen, Xinjian
• Is Part Of: Physics in medicine & biology. Volume 64:Number 17(2019:Sep.)
• Publication Date: 2019-09-04
• Subjects: optical coherence tomography -- speckle reduction -- deep learning -- residual learning; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab3556
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can learn from training data instead of manually selecting parameters such as noise level. The proposed deep convolutional neural network (CNN) applies strategies including residual learning, shortcut connection, batch normalization and leaky rectified linear units to achieve good despeckling performance. Application of the proposed method to the OCT images shows great improvement in both visual quality and quantitative indices. The proposed method provides good generalization ability for different types of retinal OCT images. It outperforms state-of-the-art methods in suppressing speckles and revealing subtle features while preserving edges.