Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images

Hanya Ahmed; Qianni Zhang; Robert Donnan; Akram Alomainy

doi:10.1117/1.JMI.11.3.034008

30 April 2024 Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images

Hanya Ahmed, Qianni Zhang, Robert Donnan, Akram Alomainy

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 3, 034008 (April 2024). https://doi.org/10.1117/1.JMI.11.3.034008

Abstract

Purpose

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.

Approach

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.

Results

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.

Conclusions

The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Hanya Ahmed, Qianni Zhang, Robert Donnan, and Akram Alomainy "Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images," Journal of Medical Imaging 11(3), 034008 (30 April 2024). https://doi.org/10.1117/1.JMI.11.3.034008

Received: 11 October 2023; Accepted: 10 April 2024; Published: 30 April 2024

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KEYWORDS

Denoising

Optical coherence tomography

Transformers

Dentistry

Speckle

Silver

Education and training

Purpose

Approach

Results

Conclusions

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