Automated quantification of parafoveal microvascular complexity using deep learning-based OCTA segmentation and...

Quantitative analysis of retinal microvasculature from optical coherence tomography angiography (OCTA) is limited by artifacts and manual segmentation variability. This study aimed to develop and validate a fully automated deep learning-based pipeline for OCTA segmentation and parafoveal microvascular complexity quantification.

This cross-sectional image-based study used 6 × 6 mm macular OCTA scans (512 × 512 pixels) obtained with the Optovue AngioVue system. A total of 132 eyes from 132 patients were included, with one eye selected randomly per patient to ensure statistical independence. A U-Net convolutional neural network (CNN) was trained on manually labeled vessel masks for vascular segmentation. Skeleton-based features-including branch count, segment count, and branch density index (BDI)-were extracted from the binarized masks. The largest avascular component was automatically detected as the foveal avascular zone (FAZ), and the parafoveal region (500 μm radius) was analyzed.

The deep learning model achieved Dice coefficient = 0.946 ± 0.012, IoU = 0.898 ± 0.021, precision = 0.927 ± 0.015, and recall = 0.967 ± 0.009 on the independent test set (n = 27), confirming robust generalizability to unseen data. The dedicated FAZ segmentation model achieved Dice = 0.648 ± 0.116 on its independent test set (n = 9). The full analysis for each image completed in < 2 s using a single GPU.

The proposed deep learning framework enables rapid, objective, and reproducible quantification of retinal microvascular complexity. Its integration into OCTA software may facilitate large-scale clinical screening and quantitative monitoring of retinal vascular health.

Not applicable.