Affiliation:
1. Department of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
2. Department of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK
Abstract
Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio—PSNR, contrast-to-noise ratio—CNR, and structural similarity index metric—SSIM). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (n = 37) and the Optic Nerve Head (ONH) (n = 4). A wide range of image quality metrics was used; PSNR and SNR that ranged between 8 and 156 dB. The minority of studies (n = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a PSNR or SNR value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low.
Reference65 articles.
1. Aumann, S., Donner, S., Fischer, J., and Müller, F. (2019). High Resolution Imaging in Microscopy and Ophthalmology, Springer International Publishing.
2. BM3D-based total variation algorithm for speckle removal with structure-preserving in OCT images;Huang;Appl. Opt.,2019
3. Optical coherence tomography angiography in diabetic retinopathy: A review of current applications;Tey;Eye Vis.,2019
4. Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications;Liefers;Transl. Vis. Sci. Technol.,2021
5. Deep learning in neural networks: An overview;Schmidhuber;Neural Netw.,2015