Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Medical Imaging Pub Date : 2024-06-01 Epub Date: 2024-04-30 DOI:10.1117/1.JMI.11.3.034008

Hanya Ahmed, Qianni Zhang, Robert Donnan, Akram Alomainy

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引用次数: 0

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.

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变压器增强型自动编码器渲染噪声光学相干断层扫描图像的净化。

目的：光学相干断层扫描（OCT）是一种新兴的医疗成像工具，通常应用于眼科视网膜疾病的检测以及其他医疗领域。OCT 图像中的噪声是一个巨大的挑战，因为它阻碍了临床医生进行详细诊断的能力：在这项工作中，我们提出了一种基于区域的深度学习去噪框架，用于自适应清理噪声 OCT 图像。该框架的核心是一个混合深度学习模型，名为变压器增强自动编码器渲染（TEAR）。利用注意门确保聚焦于前景去噪和去除背景。TEAR 的设计目的是去除 OCT 图像中常见的各种噪声伪影，提高视觉质量：对 TEAR 的性能进行了广泛的定量评估，并将其与深度学习算法和传统的最先进去噪算法进行了比较。对于牙科数据集，所提出的方法将峰值信噪比提高到 27.9 dB，CNR 提高到 6.3 dB，SSIM 提高到 0.9，等效外观数提高到 120.8 dB。对于视网膜数据集，相同序列的性能指标分别为分别为 24.6、14.2、0.64 和 1038.7 dB：结果表明，该方法能有效去除斑点噪声，其质量优于几种著名的去噪器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.10

自引率

4.20%

发文量

期刊介绍： JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.