Department of Computer Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Sci Rep. 2022 Aug 17;12(1):13975. doi: 10.1038/s41598-022-18206-8.
Microaneurysms (MAs) are pathognomonic signs that help clinicians to detect diabetic retinopathy (DR) in the early stages. Automatic detection of MA in retinal images is an active area of research due to its application in screening processes for DR which is one of the main reasons of blindness amongst the working-age population. The focus of these works is on the automatic detection of MAs in en face retinal images like fundus color and Fluorescein Angiography (FA). On the other hand, detection of MAs from Optical Coherence Tomography (OCT) images has 2 main advantages: first, OCT is a non-invasive imaging technique that does not require injection, therefore is safer. Secondly, because of the proven application of OCT in detection of Age-Related Macular Degeneration, Diabetic Macular Edema, and normal cases, thanks to detecting MAs in OCT, extensive information is obtained by using this imaging technique. In this research, the concentration is on the diagnosis of MAs using deep learning in the OCT images which represent in-depth structure of retinal layers. To this end, OCT B-scans should be divided into strips and MA patterns should be searched in the resulted strips. Since we need a dataset comprising OCT image strips with suitable labels and such large labelled datasets are not yet available, we have created it. For this purpose, an exact registration method is utilized to align OCT images with FA photographs. Then, with the help of corresponding FA images, OCT image strips are created from OCT B-scans in four labels, namely MA, normal, abnormal, and vessel. Once the dataset of image strips is prepared, a stacked generalization (stacking) ensemble of four fine-tuned, pre-trained convolutional neural networks is trained to classify the strips of OCT images into the mentioned classes. FA images are used once to create OCT strips for training process and they are no longer needed for subsequent steps. Once the stacking ensemble model is obtained, it will be used to classify the OCT strips in the test process. The results demonstrate that the proposed framework classifies overall OCT image strips and OCT strips containing MAs with accuracy scores of 0.982 and 0.987, respectively.
微动脉瘤（MAs）是有助于临床医生在早期发现糖尿病视网膜病变（DR）的特征性标志。由于其在 DR 筛查过程中的应用，自动检测视网膜图像中的 MA 是一个活跃的研究领域，DR 是工作年龄段人群失明的主要原因之一。这些工作的重点是在眼底彩色和荧光素血管造影（FA）等正视视网膜图像中自动检测 MA。另一方面，从光学相干断层扫描（OCT）图像中检测 MA 有 2 个主要优势：首先，OCT 是一种非侵入性成像技术，不需要注射，因此更安全。其次，由于 OCT 在检测年龄相关性黄斑变性、糖尿病性黄斑水肿和正常病例中的应用得到证实，通过在 OCT 中检测 MA，可以通过使用这种成像技术获得广泛的信息。在这项研究中，重点是使用深度学习在代表视网膜层深度结构的 OCT 图像中诊断 MA。为此，OCT B 扫描应该分为条带，并在得到的条带中搜索 MA 模式。由于我们需要一个包含具有合适标签的 OCT 图像条带的数据集，而这种大型标记数据集尚不可用，因此我们创建了它。为此，利用精确的配准方法将 OCT 图像与 FA 照片对齐。然后，借助相应的 FA 图像，从 OCT B 扫描中创建四个标签的 OCT 图像条带，即 MA、正常、异常和血管。一旦准备好图像条带数据集，就会使用四个经过微调的预训练卷积神经网络的堆叠泛化（堆叠）集成来训练将 OCT 图像条带分类为上述类别的模型。FA 图像仅用于创建训练过程中的 OCT 条带，此后不再需要它们。一旦获得堆叠集成模型，就将其用于测试过程中的 OCT 条带分类。结果表明，所提出的框架分别以 0.982 和 0.987 的准确度分数对整体 OCT 图像条带和包含 MA 的 OCT 条带进行分类。
