An AI-Assisted Protocol for Quantifying Superficial Chorioallantoic Membrane Vasculature

The chicken chorioallantoic membrane (CAM) is a widely used in vivo model for studying angiogenesis and tumor growth in accordance with the 3Rs principles. However, its multilayered vascular organization complicates quantitative analysis, because overlapping superficial and deeper vascular compartments are difficult to distinguish in conventional two-dimensional imaging. Although artificial intelligence (AI)-based methods improve vascular quantification, many existing workflows require extensive preprocessing or user-side model training.

We established an AI-assisted protocol that restricts vascular quantification to the superficial CAM capillary plexus, the vascular compartment most responsive to angiogenic and anti-angiogenic stimuli. Functional separation of this layer was achieved by intra-CAM injection of commercially available bovine whipped cream (minimum 30% fat), creating a diffuse white background that optically masks deeper vessels without altering vascular morphology. A U-Net-based model trained on manually annotated vessel masks was used for automated two-dimensional quantification of vessel area, length, branching points, and thickness.

The segmentation model achieved a Dice similarity coefficient of 0.831. Application at embryonic days 11 and 15 revealed remodeling of the superficial CAM vasculature, including increased branching and changes in perfusion area. The workflow reduced preprocessing complexity and enabled standardized analysis using a pre-trained segmentation model.

This method combines experimental layer isolation with automated vessel segmentation to enable two-dimensional quantification of superficial CAM vasculature without requiring custom model training. It supports practical and reproducible CAM angiogenesis studies by providing a standardized analysis workflow.