Disentanglement Network for Unsupervised Speckle Reduction of Optical Coherence Tomography Images

Abstract

Optical coherence tomography (OCT) has received increasing attention in the diagnosis of ophthalmic diseases due to its non-invasive character. However, the speckle noise associated with the low-coherence interferometric imaging modality has considerably negative influence on its clinical application. Moreover, the lack of clean and corresponding noisy OCT image pairs makes it difficult for supervised learning-based approaches to achieve satisfactory denoising results. Therefore, inspired by the idea of disentangled representation and generative adversarial network (GAN), we propose an unsupervised OCT image speckle reduction algorithm which firstly disentangles the noisy image into content and noise spaces by corresponding encoders. Then the generator is used to predict denoised OCT image only with the extracted content features. In addition, the pure noise patches which are cut from the noisy image are utilized to ensure a purer disentanglement. Extensive experiments have been conducted and the results suggest that our proposed method demonstrates competitive performance with respect to other state-of-the-art approaches.