IEEE J Biomed Health Inform. 2019 Jan;23(1):253-263. doi: 10.1109/JBHI.2018.2795545. Epub 2018 Feb 12.
Optical Coherence Tomography (OCT) is beco-ming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, we propose a surrogate-assisted classification method to classify retinal OCT images automatically based on convolutional neural networks (CNNs). Image denoising is first performed to reduce the noise. Thresholding and morphological dilation are applied to extract the masks. The denoised images and the masks are then employed to generate a lot of surrogate images, which are used to train the CNN model. Finally, the prediction for a test image is determined by the average of the outputs from the trained CNN model on the surrogate images. The proposed method has been evaluated on different databases. The results (AUC of 0.9783 in the local database and AUC of 0.9856 in the Duke database) show that the proposed method is a very promising tool for classifying the retinal OCT images automatically.
光学相干断层扫描（OCT）正成为非侵入性评估视网膜眼部疾病的最重要方法之一。随着获得的 OCT 体积数量的增加，自动进行 OCT 图像分析变得越来越重要。在本文中，我们提出了一种基于卷积神经网络（CNN）的代理辅助分类方法，用于自动分类视网膜 OCT 图像。首先进行图像去噪以降低噪声。然后应用阈值处理和形态学膨胀来提取蒙版。接下来，使用去噪图像和蒙版来生成大量的代理图像，这些图像用于训练 CNN 模型。最后，通过在代理图像上对训练好的 CNN 模型的输出进行平均来确定测试图像的预测。所提出的方法已经在不同的数据库上进行了评估。结果（本地数据库的 AUC 为 0.9783，杜克数据库的 AUC 为 0.9856）表明，该方法是一种非常有前途的自动分类视网膜 OCT 图像的工具。
