Automated anatomically guided quality assessment for OCTA via multi-region analysis and statistical calibration

Reliable optical coherence tomography angiography (OCTA) requires not only high-resolution acquisition but also standardized image quality control to ensure accurate vascular quantification. However, existing quality assessment approaches largely rely on subjective grading or global image descriptors and do not account for the region-dependent characteristics of OCTA decorrelation signals. Here, we propose an anatomically informed OCTA quality assessment framework that integrates multi-region segmentation with statistically calibrated semi-supervised learning. The segment anything model is employed to partition each image into large vessels, capillary networks, and the foveal avascular zone (FAZ), enabling that region-specific evaluation accounts for heterogeneous artifact sensitivity and optical signal formation mechanisms. Physically interpretable metrics, including vessel edge sharpness, contrast-to-noise ratio, and signal-to-noise ratio, are extracted to construct a decorrelation-grounded feature space. A distribution-calibrated grading strategy with a modulation factor of 0.9 is introduced to support stable grading under limited annotations. Evaluated on the public OCTA-500 dataset and an independent clinical dataset, the framework achieves an average Dice coefficient of 81.21 percent for region segmentation and a grading accuracy of 90.0 percent with a Cohen kappa of 0.864. By transforming global heuristic scoring into anatomically resolved and measurement-consistent evaluation, the framework supports automated data filtering for AI training pipelines and can be integrated into OCT acquisition workflows for device-level performance calibration and standardized quality control.