Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in ex...

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Bibliographic Details
Published in:Journal of medical imaging (Bellingham, Wash.) Vol. 11; no. 3; p. 034008
Main Authors: Ahmed, Hanya, Zhang, Qianni, Donnan, Robert, Alomainy, Akram
Format: Journal Article
Language:English
Published: United States 01.05.2024
Subjects:
deep learning
transformers
ophthalmology
dentistry
optical coherence tomography
image denoising
ISSN:2329-4302
Online Access:Get more information
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Summary:Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in extensive detail. In this work, a region-based, deep-learning, denoising framework is proposed for adaptive cleaning of noisy OCT-acquired images. The core of the framework is a hybrid deep-learning model named transformer enhanced autoencoder rendering (TEAR). Attention gates are utilized to ensure focus on denoising the foreground and to remove the background. TEAR is designed to remove the different types of noise artifacts commonly present in OCT images and to enhance the visual quality. Extensive quantitative evaluations are performed to evaluate the performance of TEAR and compare it against both deep-learning and traditional state-of-the-art denoising algorithms. The proposed method improved the peak signal-to-noise ratio to 27.9 dB, CNR to 6.3 dB, SSIM to 0.9, and equivalent number of looks to 120.8 dB for a dental dataset. For a retinal dataset, the performance metrics in the same sequence are: 24.6, 14.2, 0.64, and 1038.7 dB, respectively. The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.
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ISSN:2329-4302
DOI:10.1117/1.JMI.11.3.034008