Ahmed Hanya, Zhang Qianni, Wong Ferranti, Donnan Robert, Alomainy Akram
Department of Electronic Engineering and Computer Science, Queen Mary University of London-QMUL, London E1 4NS, UK.
Institute of Dentistry at Barts Health, Queen Mary University of London-QMUL, London E1 4NS, UK.
J Imaging. 2023 Nov 7;9(11):244. doi: 10.3390/jimaging9110244.
Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for the detection of retinal diseases and in dentistry for the early detection of tooth decay. Speckle noise is ubiquitous in OCT images, which can hinder diagnosis by clinicians. In this paper, a region-based, deep learning framework for the detection of anomalies is proposed for OCT-acquired images. The core of the framework is Transformer-Enhanced Detection (TED), which includes attention gates (AGs) to ensure focus is placed on the foreground while identifying and removing noise artifacts as anomalies. TED was designed to detect the different types of anomalies commonly present in OCT images for diagnostic purposes and thus aid clinical interpretation. Extensive quantitative evaluations were performed to measure the performance of TED against current, widely known, deep learning detection algorithms. Three different datasets were tested: two dental and one CT (hosting scans of lung nodules, livers, etc.). The results showed that the approach verifiably detected tooth decay and numerous lesions across two modalities, achieving superior performance compared to several well-known algorithms. The proposed method improved the accuracy of detection by 16-22% and the Intersection over Union (IOU) by 10% for both dentistry datasets. For the CT dataset, the performance metrics were similarly improved by 9% and 20%, respectively.
光学相干断层扫描（OCT）是医疗保健领域中一种新兴的成像工具，在眼科中常用于检测视网膜疾病，在牙科中用于早期检测龋齿。散斑噪声在OCT图像中普遍存在，这可能会妨碍临床医生的诊断。本文针对OCT采集的图像，提出了一种基于区域的深度学习异常检测框架。该框架的核心是Transformer增强检测（TED），它包括注意力门（AG），以确保在将噪声伪影识别并作为异常去除的同时，将注意力集中在前景上。TED旨在检测OCT图像中通常存在的不同类型的异常，以用于诊断目的，从而辅助临床解读。进行了广泛的定量评估，以衡量TED相对于当前广为人知的深度学习检测算法的性能。测试了三个不同的数据集：两个牙科数据集和一个CT数据集（包含肺结节、肝脏等的扫描图像）。结果表明，该方法能够可靠地检测出两种模式下的龋齿和大量病变，与几种知名算法相比性能更优。对于两个牙科数据集，所提出的方法将检测准确率提高了16 - 22%，交并比（IOU）提高了10%。对于CT数据集，性能指标同样分别提高了9%和20%。
