Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an ~10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.
翻译:光学成像学(OCTA)是一个新颖的成像模式,广泛用于眼科和神经科学研究,以观察视网膜容器和显微血管系统。然而,公开提供的OCTA数据集仍然稀缺。在本文中,我们引入了最大和最全面的OCTA数据集,该数据集由500个主题组成,在两个观察领域(FOVs)下包含OCTA成像。该数据集提供了丰富的图像和说明,包括两种模式(OCT/OCTA卷)、六种预测、四种文本标签(年龄/性别/眼/2特征/疾病)和七种分解标签(大船/毛球/Artery/vein/2D FAFA/3D FAZ/retinal层)。然后,我们提出一个名为CAVFF的多颗粒分解任务,将毛线断裂、动脉断裂、静脉切分解和FAFZ分解在一个统一框架内进行。此外,我们优化了3D-OOD图像网络(年龄/OD数据预测网络,在IPN2级数据库中显示一个分析结果。