Automated Artifacts and Noise Removal from Optical Coherence Tomography Images Using Deep Learning Technique

Akter, N; Perry, S; Fletcher, J; Simunovic, M; Roy, M

Automated Artifacts and Noise Removal from Optical Coherence Tomography Images Using Deep Learning Technique

Akter, N Perry, S

Fletcher, J Simunovic, M Roy, M

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021
Issue Date:: 2021-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 1 Jan 2023

Adobe PDF

Download Accepted ManuscriptAdobe PDF (1.43 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Akter, N
dc.contributor.author	Perry, S https://orcid.org/0000-0002-2794-3178
dc.contributor.author	Fletcher, J
dc.contributor.author	Simunovic, M
dc.contributor.author	Roy, M
dc.date	2020-12-01
dc.date.accessioned	2021-01-05T06:15:35Z
dc.date.available	2021-01-05T06:15:35Z
dc.date.issued	2021-01-01
dc.identifier.citation	2021
dc.identifier.uri	http://hdl.handle.net/10453/145086
dc.description.abstract	Optical Coherence Tomography (OCT) is a popular non-invasive clinical tool for the diagnosis of ocular diseases that provides micron-scale images of ocular pathology in vivo and in real-time. The cross-sectional OCT B-scan of Temporal-Superior- Nasal-Inferior-Temporal (TSNIT) peripapillary retinal profile is widely used to diagnose and monitor glaucoma. However, raw OCT images can be marred by noise and artifacts, especially vitreoretinal interface opacity: this can lead to segmentation error, misinterpretation of retinal thickness measurements and possibly inappropriate glaucoma management. In this study, we designed and trained a U-Net model on OCT B-scans with artifacts, and their corresponding ‘artifact-free B-scans’. The U-Net was able to remove the artifacts successfully with better performance in terms of PSNR and SSIM values. The SNR of the OCT scans with speckle noise associated with artifacts has also been improved. To the best of our knowledge, this is the first study where automated vitreous opacity artifact removal has been applied to the TSNIT profile. The performance of the U-net model on measures such as PSNR, SSIM, MAE, and MSE is compared with the state-of-the-art image denoising models. It is observed that the proposed U-Net model performs better as compared to the other models on both parametric and visual evaluations. In the future, this U-Net model could be used to solve automatic retinal layer segmentation errors and assist clinicians in interpreting OCT images in glaucoma diagnosis and monitoring.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence
dc.rights	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Automated Artifacts and Noise Removal from Optical Coherence Tomography Images Using Deep Learning Technique
dc.type	Conference Proceeding
utslib.location.activity	Canberra, ACT, Australia
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
utslib.copyright.embargo	2023-01-01T00:00:00+1000Z
dc.date.updated	2021-01-05T06:15:25Z
pubs.finish-date	2020-12-04
pubs.publication-status	Published online
pubs.start-date	2020-12-01

Abstract:

Optical Coherence Tomography (OCT) is a popular non-invasive clinical tool for the diagnosis of ocular diseases that provides micron-scale images of ocular pathology in vivo and in real-time. The cross-sectional OCT B-scan of Temporal-Superior- Nasal-Inferior-Temporal (TSNIT) peripapillary retinal profile is widely used to diagnose and monitor glaucoma. However, raw OCT images can be marred by noise and artifacts, especially vitreoretinal interface opacity: this can lead to segmentation error, misinterpretation of retinal thickness measurements and possibly inappropriate glaucoma management. In this study, we designed and trained a U-Net model on OCT B-scans with artifacts, and their corresponding ‘artifact-free B-scans’. The U-Net was able to remove the artifacts successfully with better performance in terms of PSNR and SSIM values. The SNR of the OCT scans with speckle noise associated with artifacts has also been improved. To the best of our knowledge, this is the first study where automated vitreous opacity artifact removal has been applied to the TSNIT profile. The performance of the U-net model on measures such as PSNR, SSIM, MAE, and MSE is compared with the state-of-the-art image denoising models. It is observed that the proposed U-Net model performs better as compared to the other models on both parametric and visual evaluations. In the future, this U-Net model could be used to solve automatic retinal layer segmentation errors and assist clinicians in interpreting OCT images in glaucoma diagnosis and monitoring.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145086