Automated Artifacts and Noise Removal from Optical Coherence Tomography Images Using Deep Learning Technique
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021
- Issue Date:
- 2021-01-01
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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.
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