Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning
Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning
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DOI：
10.1111/aos.14353
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发表时间：
2020-01-24
影响因子：
3.4
通讯作者：
Rittscher, Jens
中科院分区：
文献类型：
--
作者：
Shah, Mital;Ledo, Ana Roomans;Rittscher, Jens
目的近年来，深度学习在眼科高发疾病的自动图像分析中的应用越来越多。我们想要确定深度学习是否可以用于使用比传统使用的更小的数据集来自动分类来自Stargardt病(STGD)患者的光学相干断层扫描(OCT)图像。方法选择60例STGD患者和33例视网膜OCT正常的患者，以中心凹为中心的单次OCT扫描作为输入数据。使用了两种方法：模型1--预先训练的卷积神经网络(CNN)；模型2--一种新的CNN结构。对两种模型的准确性、敏感性、特异性和Jaccard相似性评分(JSS)进行评价。结果从视网膜OCT正常的受试者中选择了102例OCT扫描，从STGD患者中选择了647例OCT扫描。当两个模型都被实施为二进制分类器时，获得的结果最高：模型1-准确性99.6%，敏感性99.8%，特异性98.0%，JSS 0.990；模型2-准确性97.9%，敏感性97.9%，特异性98.0%，JSS 0.976。结论本研究中使用的深度学习分类模型能够达到较高的准确率，尽管使用的数据集比传统使用的小，并且在区分正常OCT扫描和STGD患者的OCT扫描方面是有效的。这一初步研究为应用深度学习对遗传性视网膜疾病患者的OCT图像进行分类提供了有希望的结果。
Purpose Recent advances in deep learning have seen an increase in its application to automated image analysis in ophthalmology for conditions with a high prevalence. We wanted to identify whether deep learning could be used for the automated classification of optical coherence tomography (OCT) images from patients with Stargardt disease (STGD) using a smaller dataset than traditionally used. Methods Sixty participants with STGD and 33 participants with a normal retinal OCT were selected, and a single OCT scan containing the centre of the fovea was selected as the input data. Two approaches were used: Model 1 - a pretrained convolutional neural network (CNN); Model 2 - a new CNN architecture. Both models were evaluated on their accuracy, sensitivity, specificity and Jaccard similarity score (JSS). Results About 102 OCT scans from participants with a normal retinal OCT and 647 OCT scans from participants with STGD were selected. The highest results were achieved when both models were implemented as a binary classifier: Model 1 - accuracy 99.6%, sensitivity 99.8%, specificity 98.0% and JSS 0.990; Model 2 - accuracy 97.9%, sensitivity 97.9%, specificity 98.0% and JSS 0.976. Conclusion The deep learning classification models used in this study were able to achieve high accuracy despite using a smaller dataset than traditionally used and are effective in differentiating between normal OCT scans and those from patients with STGD. This preliminary study provides promising results for the application of deep learning to classify OCT images from patients with inherited retinal diseases.