Computational Quantification of Mouse Retinal Vasculature Using ImageJ.

Postnatal mouse retinal vascular development is a widely used model for studying retinal vascular diseases and evaluating candidate therapies. This is particularly relevant for inherited disorders such as familial exudative vitreoretinopathy (FEVR), in which impaired vascular growth and organization are central to disease pathogenesis. Numerous approaches have been used to assess retinal vasculature in mouse flat mounts, ranging from qualitative descriptions to limited quantitative measurements of vascular growth. However, phenotypic variability across genetic models, including different models of FEVR, complicates comparisons and underscores the need for standardized, comprehensive multi-parameter analyses that are suitable for rapid and cost-effective screening studies. We describe a standardized morphometric protocol using ImageJ software to quantitatively analyze mouse retinal vasculature in a reproducible manner. The protocol begins with measurement of areas of vascular disorganization (meshes) as well as total vascular and retinal area. Two defined regions in the peripheral and midperipheral retina are then selected to quantify cell clusters, followed by image processing, binarization, and skeletonization. From these processed images, vascular density, branch number, branch length and thickness, junction number, triple points, and box-counting fractal dimension and lacunarity are quantified. Overall, this protocol provides a rapid, cost-effective, and standardized framework for quantifying retinal vascular phenotypes across diverse mouse models. By capturing multiple structural features and accommodating phenotypic variability, it is well-suited for comparative studies and therapeutic screening in retinal vascular disease. Key features • Computational method for mouse retina vessel image analysis for multi-parameter vascular quantification for user-selected regions of interest. • Free open-source ImageJ-based workflow combining disorganization mapping, skeletonization, and fractal analysis for reproducible vascular network characterization. • Optimized for rapid, cost-effective screening of structural vascular outcomes across developmental stages, disease states, and therapeutic interventions.