A novel deep learning based OCTA de-striping method

Dongxu Gao*, Numan Celik, Xiyin Wu, Bryan M. Williams, Amira Stylianides, Yalin Zheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Noise in images presents a considerable problem, limiting their readability and hindering the performance of post-processing and analysis tools. In particular, optical coherence tomography angiography (OCTA) suffers from stripe noise. In medical imaging, clinicians rely on high quality images in order to make accurate diagnoses and plan management. Poor quality images can lead to pathology being overlooked or undiagnosed. Image denoising is a fundamental technique that can be developed to tackle this problem and improve performance in many applications, yet there exists no method focused on removing stripe noise in OCTA. Existing OCTA denoising methods do not consider the structure of stripe noise, which severely limits their potential for recovering the image. The development of artificial intelligence (AI) have enabled deep learning approaches to obtain impressive results and play a dominant role in many areas, but require a ground truth for training, which is difficult to obtain for this problem. In this paper, we propose a revised U-net framework for removing the stripe noise from OCTA images, leaving a clean image. With our proposed method, a ground truth is not required for training, allowing both the stripe noise and the clean image to be estimated, preserving more image detail without compromising image quality. The experimental results show the impressive de-striping performance of our method on OCTA images. We evaluate the effectiveness of our proposed method using the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), achieving excellent results as well.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings
EditorsYalin Zheng, Bryan M. Williams, Ke Chen
Number of pages9
ISBN (Print)9783030393427
Publication statusPublished - 24 Jan 2020
Event23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 - Liverpool, United Kingdom
Duration: 24 Jul 201926 Jul 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Country/TerritoryUnited Kingdom


  • deep learning
  • image decomposition
  • OCTA
  • stripe noise removal


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