A novel deep learning based OCTA de-striping method

Gao, D ORCID: 0000-0001-7008-0737, Celik, N ORCID: 0000-0003-1813-1036, wu, xiyin, Williams, bryan ORCID: 0000-0001-5930-287X, Stylianides, A and Zheng, yalin ORCID: 0000-0002-7873-0922 (2020) A novel deep learning based OCTA de-striping method. .

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Abstract

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.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	OCTA, Stripe noise removal, Image decomposition, Deep learning
Depositing User:	Symplectic Admin
Date Deposited:	08 Jun 2020 08:52
Last Modified:	18 Jan 2023 23:50
DOI:	10.1007/978-3-030-39343-4_16
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3089648