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使用生成式深度学习的光学相干断层扫描脉络膜增强

Optical coherence tomography choroidal enhancement using generative deep learning.

作者信息

Bellemo Valentina, Kumar Das Ankit, Sreng Syna, Chua Jacqueline, Wong Damon, Shah Janika, Jonas Rahul, Tan Bingyao, Liu Xinyu, Xu Xinxing, Tan Gavin Siew Wei, Agrawal Rupesh, Ting Daniel Shu Wei, Yong Liu, Schmetterer Leopold

机构信息

Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore.

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

出版信息

NPJ Digit Med. 2024 May 4;7(1):115. doi: 10.1038/s41746-024-01119-3.

Abstract

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.

摘要

光谱域光学相干断层扫描(SDOCT)是临床眼科成像的金标准。然而,这类设备的穿透深度有限,而脉络膜的可视化对于诊断脉络膜视网膜疾病至关重要,但目前仍然受限。虽然扫频源光学相干断层扫描(SSOCT)设备能够实现脉络膜的可视化,但这些仪器价格昂贵,实际应用中的可用性也有限。我们提出了一种基于人工智能(AI)的解决方案,以增强光学相干断层扫描(OCT)扫描中脉络膜的可视化,并使用生成式深度学习(DL)实现脉络膜指标的定量测量。利用匹配图像在训练过程中学习深层解剖特征,生成了具有改善的脉络膜可见性的合成增强型SDOCT B扫描图像。我们使用一个单中心三级眼科护理机构队列,该队列共有362名SDOCT - SSOCT配对受试者,用来自410只健康眼睛、192只青光眼眼睛和133只糖尿病视网膜病变眼睛的150,784张图像训练我们的模型。部署了一个来自146只眼睛的37,376张图像的独立外部测试数据集,以评估合成增强型SDOCT图像的真实性和质量。专家区分真实图像与合成图像的能力较差(准确率为47.5%)。参考SSOCT和合成增强型SDOCT测量的脉络膜厚度、面积、体积和血管指数显示,皮尔逊相关系数分别高达0.97 [95%置信区间:0.96 - 0.98]、0.97 [0.95 - 0.98]、0.95 [0.92 - 0.98]和0.87 [0.83 - 0.91],组内相关值分别为0.99 [0.98 - 0.99]、0.98 [0.98 - 0.99]、0.95 [0.96 - 0.98]和0.93 [0.91 - 0.95]。因此,我们的深度学习生成模型成功生成了与SSOCT图像难以区分的逼真增强型SDOCT数据,提供了更好的脉络膜可视化效果。这项技术能够对以前受SDOCT成像深度限制的脉络膜指标进行准确测量。这些发现为在健康和病理条件下利用价格合理的SDOCT设备研究脉络膜开辟了新的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c367/11069520/eb371f7c4de6/41746_2024_1119_Fig1_HTML.jpg

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