Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.

Significance: Automated segmentation of periarterial and perivenous capillary-free zones (CFZs) in optical coherence tomography angiography (OCTA) can significantly improve early detection and monitoring of diabetic retinopathy (DR), a leading cause of vision impairment, by identifying subtle microvascular changes. Aim: We aimed to develop and evaluate deep learning models, including convolutional neural networks (CNNs) and vision transformers (ViTs), for precise segmentation of periarterial and perivenous CFZs. Quantitative features derived from the segmented CFZs were assessed as potential biomarkers for DR. Approach: OCTA images from healthy controls, patients with diabetes but no DR (NoDR), and those with mild DR were utilized. Automated CFZ maps were generated using deep learning models such as UNet, UNet++, TransUNet, and Segformer. Quantitative features, including CFZ ratios, counts, and mean sizes, were analyzed to characterize disease progression. Results: UNet++ with EfficientNet-b7 achieved the best performance, with a mean intersection over union of 86.48% and a Dice coefficient of 89.87%. Quantitative analyses revealed significant differences in CFZ metrics between the control, NoDR, and mild DR groups, demonstrating their potential as sensitive biomarkers for early DR detection and monitoring. Conclusions: The study underscores the efficacy of deep learning models in automating CFZ segmentation and introduces quantitative features as biomarkers for DR. These findings support further exploration of CFZ analysis in retinal disease diagnostics and therapeutic monitoring.