Novel semi-automated system for multi-dimensional analysis of macular neovascularization: quantitative biomarkers and rule-based morphological categorization across OCTA platforms.

PURPOSE: To develop and validate a novel semi-automated Python-based pipeline for multi-dimensional analysis of macular neovascularization (MNV) on optical coherence tomography angiography (OCTA) images. The system provides standardized biomarkers, rule-based morphological categorization, and morphology-derived interpretive labels. It also incorporates an automated morphological classification scheme to objectively distinguish active, mature quiescent, transitional, and arteriolarized states. METHODS: We developed an advanced image-processing pipeline incorporating hybrid multiscale vessel enhancement using Frangi/tubeness and Laplacian of Gaussian filters, hybrid binarization (LoG-Otsu and tubeness-Sauvola with small-particle removal), dynamic region-of-interest refinement, and boundary-branch exclusion. The system generates a Standardized Vascular Complexity Score, a Standardized Caliber Uniformity Score, and a Standardized Maturity Index on a 0-100 scale, and performs rule-based classification into Medusa, Seafan, Glomerular, Tree in bud, and Dead tree patterns using predefined percentile and trunk-pattern thresholds. Morphology-derived interpretive categories - Active pattern, Maturequiescentpattern, Transitional-pattern, and Arteriolarized pattern - were assigned using quantitative thresholds and published morphological criteria. A total of 112 MNV lesions acquired on three OCTA platforms (Zeiss PlexElite 6 × 6 mm, Zeiss CIRRUS AngioPlex HD 3 × 3 mm, and Optovue Solix 6 × 6 mm) were analyzed. Masked expert-automated morphological agreement was assessed in a stratified subset (n = 54). Inter-observer reproducibility was evaluated with three operators in 46 lesions using ICC(2,1), and same-operator intra-observer test-retest reproducibility was assessed as a supplement (n = 46). Between-stratum score differences were summarized using Kruskal-Wallis tests and ε² effect sizes with bootstrap 95% confidence intervals. RESULTS: Raw topological metrics differed substantially by device, precluding direct numerical comparison. Device-specific standardization using principal component analysis and piecewise-linear normalization yielded convergent standardized Complexity, Caliber Uniformity, and Maturity scores on a common 0-100 reporting scale across devices, with median values of approximately 50. Under this standardized framework, Complexity Score, Caliber Uniformity Score, and Maturity Index did not differ significantly across strata (Kruskal-Wallis p = 0.425, 0.572, and 0.582, respectively; ε² ≈ 0). Automated morphological classification identified the full spectrum of MNV subtypes across all platforms. Agreement with masked expert grading was modest to moderate, with an overall agreement of 57.4% and a quadratic weighted κ of 0.507. Inter-observer ICC(2,1) was good for lesion area (0.859), Complexity Score (0.807), and Standardized Caliber Uniformity Score (0.770), and moderate for the Maturity Index derived from the Caliber score (0.593). CONCLUSION: This system provides a transparent and robust platform for quantification and rule-based morphological reporting of MNV architecture across OCTA platforms, establishing a common framework for comparative analysis. Future outcome-linked and multi-center evaluations will be required to establish clinical utility. By integrating morphological patterns with quantitative biomarkers and automated classification, the system enables objective differentiation of active angiogenesis from mature remodeling.