Treder Maximilian, Lauermann Jost Lennart, Eter Nicole
Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany.
Graefes Arch Clin Exp Ophthalmol. 2018 Feb;256(2):259-265. doi: 10.1007/s00417-017-3850-3. Epub 2017 Nov 20.
Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT).
A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD.
After an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p < 0.001).
With a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.
我们的目的是利用深度学习在光谱域光学相干断层扫描（SD-OCT）中自动检测年龄相关性黄斑变性（AMD）。
本研究使用了1112例渗出性AMD患者和一个健康对照组的横断面SD-OCT图像。第一步，使用一个开源的多层深度卷积神经网络（DCNN），该网络用来自ImageNet的120万张图像进行了预训练，并使用1012次横断面SD-OCT扫描（AMD：701例；健康：311例）进行训练和验证。在此过程中计算训练准确率、验证准确率和交叉熵。使用开源深度学习框架TensorFlow™（谷歌公司，美国加利福尼亚州山景城）来加速深度学习过程。在最后一步，使用上述深度学习过程的信息创建的DCNN分类器在检测100张未经训练的横断面SD-OCT图像（AMD：50例；健康：50例）时进行测试。因此，计算了一个AMD测试分数：AMD的分数假定为0.98或更高。
经过500个训练步骤的迭代，训练准确率和验证准确率均为100%，交叉熵为0.005。AMD测试组的平均AMD分数为0.997±0.003，健康对照组为0.9203±0.085。两组之间的差异非常显著（p<0.001）。
使用基于TensorFlow™的深度学习方法，可以在SD-OCT中高灵敏度和特异性地检测AMD。有了更多的图像数据，这个分类器有可能扩展用于其他黄斑疾病或AMD的更多细节，这表明该模型可用于临床决策支持。未来另一个可能的应用将涉及通过自动检测隐藏的图像信息对不同疾病治疗的进展和成功进行个体预测。
