Wong Damon W K, Yow Ai Ping, Tan Bingyao, Xinwen Yao, Chua Jacqueline, Schmetterer Leopold
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1875-1878. doi: 10.1109/EMBC44109.2020.9175868.
Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance- Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.
光学相干断层扫描（OCT）能够对眼睛进行体内容积成像。在OCT的表面成像中识别和定位解剖特征是基于OCT的图像分析中的重要步骤。然而，在结构OCT或血管OCT血管造影中，解剖特征的可见性是有限的。在本文中，我们建议使用血管增强的OCT表面图像，基于卷积网络的FasterRCNN目标检测框架同时检测解剖特征。应用迁移学习来调整预训练模型作为骨干网络。在一个包含419幅图像的数据集上对模型进行了评估。结果表明，VGG-FasterRCNN的平均精度为0.77，黄斑和视盘的定位误差分别为0.18±0.10毫米和0.24±0.13毫米。结果很有前景，表明该网络有可能用于自动同时检测解剖特征。临床相关性——OCT图像分析协议的自动化需要在OCT表面成像中定位解剖特征。使用快速检测网络可能意味着在图像采集过程中进行基于图像的实时跟踪。
