IEEE Trans Med Imaging. 2021 Mar;40(3):928-939. doi: 10.1109/TMI.2020.3042802. Epub 2021 Mar 2.
Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE), which consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. This dataset with the source code has been released for public access to assist researchers in the community in undertaking research in related topics. Secondly, we introduce a novel split-based coarse-to-fine vessel segmentation network for OCTA images (OCTA-Net), with the ability to detect thick and thin vessels separately. In the OCTA-Net, a split-based coarse segmentation module is first utilized to produce a preliminary confidence map of vessels, and a split-based refined segmentation module is then used to optimize the shape/contour of the retinal microvasculature. We perform a thorough evaluation of the state-of-the-art vessel segmentation models and our OCTA-Net on the constructed ROSE dataset. The experimental results demonstrate that our OCTA-Net yields better vessel segmentation performance in OCTA than both traditional and other deep learning methods. In addition, we provide a fractal dimension analysis on the segmented microvasculature, and the statistical analysis demonstrates significant differences between the healthy control and Alzheimer's Disease group. This consolidates that the analysis of retinal microvasculature may offer a new scheme to study various neurodegenerative diseases.
光学相干断层扫描血管造影术(OCTA)是一种非侵入性成像技术,已越来越多地用于以毛细血管水平分辨率成像视网膜血管。然而,由于毛细血管可视性低和血管复杂性高等各种挑战,OCTA 中视网膜血管的自动分割研究不足,尽管其在理解许多与视力相关的疾病方面具有重要意义。此外,没有用于训练和验证分割算法的具有手动分级血管的公共 OCTA 数据集。为了解决这些问题,我们首次在视网膜图像分析领域构建了一个专门的视网膜 OCTA 分割数据集(ROSE),该数据集由 229 张 OCTA 图像组成,这些图像具有中心线级或像素级别的血管注释。该数据集及其源代码已公开发布,供公众访问,以帮助社区中的研究人员从事相关主题的研究。其次,我们引入了一种用于 OCTA 图像的基于分割的粗到细血管分割网络(OCTA-Net),该网络能够分别检测厚血管和细血管。在 OCTA-Net 中,首先利用基于分割的粗分割模块生成血管的初步置信图,然后使用基于分割的细化分割模块优化视网膜微血管的形状/轮廓。我们在构建的 ROSE 数据集上对最先进的血管分割模型和我们的 OCTA-Net 进行了全面评估。实验结果表明,我们的 OCTA-Net 在 OCTA 中的血管分割性能优于传统方法和其他深度学习方法。此外,我们对分割的微血管进行了分形维数分析,统计分析表明健康对照组和阿尔茨海默病组之间存在显著差异。这表明对视网膜微血管的分析可能为研究各种神经退行性疾病提供新方案。