OCTA-500: A retinal dataset for optical coherence tomography angiography study.

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 about 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.

Priors-guided convolutional neural network for 3D foveal avascular zone segmentation.

The foveal avascular zone (FAZ) is sensitive to retinal pathological process in the macular fovea area. For the purpose of efficient FAZ 3D quantification, we firstly propose a priors-guided convolutional neural network (CNN) to provide a tailor-made solution for 3D FAZ segmentation for optical coherence tomography angiography (OCTA) images. Location and topology priors are taken into account. The random central crop module is utilized to restrict the region to be processed, while the non-local attention gates are contained in the network to capture long-range dependency. The topological consistency constraint is calculated on maximum and mean projection maps through persistent homology to keep topological correctness of the model's prediction. Our method was evaluated on two OCTA datasets with 478 eyes and the experimental results demonstrate that our method can not only alleviate the over-segmentation prominently but also fit better on the contour of FAZ region.

Self-Supervised Sequence Recovery for Semi-Supervised Retinal Layer Segmentation.

Automated layer segmentation plays an important role for retinal disease diagnosis in optical coherence tomography (OCT) images. However, the severe retinal diseases result in the performance degeneration of automated layer segmentation approaches. In this paper, we present a robust semi-supervised layer segmentation network to relieve the model failures on abnormal retinas. We obtain the lesion features from the labeled images with disease-balanced distribution, and utilize the unlabeled images to supplement the layer structure information. Specifically, in our method, the cross-consistency training is utilized over the predictions of different decoders, and we enforce a consistency between different decoder predictions to improve the encoder's representation. Then, we propose a sequence prediction branch based on self-supervised manner, which is designed to predict the position of each jigsaw puzzle to obtain sensory perception of the retinal layer structure. To this task, a layer spatial pyramid pooling (LSPP) module is designed to extract multi-scale layer spatial features. Furthermore, we use the optical coherence tomography angiography (OCTA) to supplement the information damaged by diseases. The experimental results illustrate that our method achieves more robust results compared with current supervised segmentation methods. Meanwhile, advanced segmentation performance can be obtained compared with state-of-the-art semi-supervised segmentation methods.

FOVEAL AVASCULAR ZONE VOLUME: A New Index Based on Optical Coherence Tomography Angiography Images.

PURPOSE: To propose a new clinical evaluation index, foveal avascular zone (FAZ) volume, and analyze its statistical significance. METHODS: A semiautomatic method is proposed to measure the FAZ volume in optical coherence tomography angiography images as follows: The region of interest was flattened and annotated axially. The labeled pixels in the restored region of interest were counted as the FAZ volume. Linear regression and the independent samples t-test were performed for the statistical analysis. RESULTS: Sixty-one normal, 64 high myopia, and 42 diabetic retinopathy eyes were imaged using optical coherence tomography angiography. For normal eyes, the FAZ volume correlates inversely with central macular thickness (superficial: P = 0.004; deep: P < 0.001) and positively with area (P < 0.001). For high myopia eyes, the deep plexus FAZ (P = 0.34) and total FAZ (P = 0.38) volumes show no significant difference, whereas the superficial plexus FAZ volume is significantly larger than control (P < 0.001). For diabetic retinopathy eyes, the superficial plexus FAZ (P = 0.001), deep plexus FAZ (P = 0.014), and total volumes (P = 0.002) are significantly larger than control. CONCLUSION: The FAZ volume is proposed for depicting the 3D structure of the FAZ. It shows greater sensitivity for vascular alteration that makes it meaningful for clinical analysis.