Related Experiment Video
Updated: Jan 17, 2026
Morphometric Analyses of Retinal Sections
Published on: February 19, 2012
Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of
Naomi Joseph1, Ved Shivade1, Jiawei Chen1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Purpose:
To develop an interactive image editor, Ophthalmic Segmentation and Analysis Software (OASIS) for automating and improving the analysis of meibography images to gain insight into the progression of meibomian gland dysfunction.
Methods:
A natural history study was conducted, collecting 2,439 meibography images from 325 patients. Clinicians used OASIS for image analysis, which involved both manual and deep-learning assisted processes. In the manual process, clinicians annotated three distinct masks per image: the eyelid, glands, and gland loss. In the assisted process, OASIS incorporated deep-learning models to infer gland masks, reducing the time required for gland-by-gland annotation. The software's interface provided additional tools for image enhancement and calculation of currently used clinical metrics including the Pult scale.
Results:
OASIS enabled clinicians to quantitatively analyze MGD in under 3 minutes, representing an 87% reduction in time compared to traditional manual analysis methods. The software accurately calculated Pult meiboscale grades with fair agreement between the clinician and software (kappa = 0.79), demonstrating a high level of consistency.
Conclusions:
OASIS significantly streamlines the analysis of meibography images and allows for a more objective and efficient evaluation of MGD. By implementing deep learning models for gland inference and providing a suite of custom annotation tools, OASIS may reduce the time burden on clinicians while maintaining accuracy.
Translational Relevance:
OASIS paves the way for developing quantitative biomarkers for MGD and may have applications in both clinical practices and research. OASIS also further demonstrates the potential for AI-driven tools in improving ophthalmic image analysis.
