Automated detection and classification of early AMD biomarkers using deep learning
Automated detection and classification of early AMD biomarkers using deep learning
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DOI：
10.1038/s41598-019-47390-3
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发表时间：
2019-07-29
影响因子：
4.6
通讯作者：
Hu, Zhihong Jewel
中科院分区：
文献类型：
--
作者：
Saha, Sajib;Nassisi, Marco;Hu, Zhihong Jewel
年龄相关性黄斑变性（AMD）影响着数百万人，是世界各地失明的主要原因。理想情况下，受影响的个体将在晚期后遗症（如外部视网膜萎缩或渗出性新生血管膜发展，这可能会导致不可逆的视力丧失）之前的早期阶段被识别。早期识别可以使患者分期，并建立适当的监测间隔。早期AMD阶段的准确分期也可以促进新的预防性疗法的开发。然而，AMD的准确和精确分期，特别是使用更新的基于光学相干断层扫描（OCT）的生物标志物可能是时间密集型的，并且需要专家培训，这在许多情况下可能是不可行的，特别是在筛查环境中。在这项工作中，我们开发了用于自动检测和分类早期AMD OCT生物标志物的深度学习方法。对深度卷积神经网络（CNN）进行了明确的训练，用于对玻璃疣内的高反射病灶、低反射病灶和来自OCT B扫描的视网膜下玻璃疣样沉积物进行自动检测和分类。进行了大量实验，以评估几种最先进的CNN和不同的迁移学习协议在包含来自153名患者的约20000次OCT B扫描的图像数据集上的性能。鉴定早期AMD生物标志物存在的总体准确率为87%。
Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individuals would be identified at an early stage before late sequelae such as outer retinal atrophy or exudative neovascular membranes develop, which could produce irreversible visual loss. Early identification could allow patients to be staged and appropriate monitoring intervals to be established. Accurate staging of earlier AMD stages could also facilitate the development of new preventative therapeutics. However, accurate and precise staging of AMD, particularly using newer optical coherence tomography (OCT)-based biomarkers may be time-intensive and requires expert training which may not feasible in many circumstances, particularly in screening settings. In this work we develop deep learning method for automated detection and classification of early AMD OCT biomarker. Deep convolution neural networks (CNN) were explicitly trained for performing automated detection and classification of hyperreflective foci, hyporeflective foci within the drusen, and subretinal drusenoid deposits from OCT B-scans. Numerous experiments were conducted to evaluate the performance of several state-of-the-art CNNs and different transfer learning protocols on an image dataset containing approximately 20000 OCT B-scans from 153 patients. An overall accuracy of 87% for identifying the presence of early AMD biomarkers was achieved.