Automated, computerized, feature-based phenotype analysis of slit lamp images of the mouse lens

Abstract

Longitudinal studies of a variety of transgenic mouse models for lens development can create substantial challenges in database management and analysis. We report a novel, automated, feature-based informatics approach to screening lens phenotypes in a large database of slit lamp images. Digital slit lamp images of normal and abnormal lenses in eyes of wild type (wt), SC1 null and SPARC null transgenic mice were recorded for quantitative evaluation of their structural phenotype. The images were processed to improve the contrast of structural features that corresponded to rings of opacity and fluctuations in scattering intensity in the lenses. Measurable attributes were assigned to the features in the lens images and given as an output vector of 46 dimensions. Characteristic patterns were correlated with the structural phenotype of each mutant and wt lens and a statistical fit for each phenotype was defined. The genotype was identified correctly in nearly 85% of the slit lamp images on the basis of an automated computer analysis of the lens structural phenotype. The automated computer algorithm has the potential to evaluate a large database of slit lamp images and distinguish mouse genotypes on the basis of lens phenotypes objectively using a neural network analysis of the structural features observed in the slit lamp images. The neural network approach is a promising technology for objective evaluation of genotype/phenotype relationships based on structural features and light scattering in lenses. Further improvements in the automated method can be expected to simplify and increase the accuracy and efficiency of the feature based analysis of structural phenotypes linked to genetic variation.