Human eyeball model reconstruction and quantitative analysis.

Area of Science:

• Ophthalmology
• Medical Imaging
• Computer Vision

Background:

• Accurate eyeball shape determination is crucial for diagnosing eye diseases such as myopia.
• Existing methods may lack precision or require manual intervention.

Purpose of the Study:

• To develop an automatic, precise approach for reconstructing the 3D geometric shape of the eyeball from MR images.
• To introduce novel metrics for detailed ocular surface analysis.

Main Methods:

• An automated pipeline involving image segmentation, registration, and B-Spline/subdivision surface fitting.
• No manual interaction was required during model development.
• Development of two new metrics: Gaussian Curvature Analysis and Sphere Distance Deviation.

Main Results:

• High-resolution 3D eyeball models were accurately reconstructed.
• The models allowed for precise quantification and analysis of geometric characteristics.
• The proposed metrics effectively quantified cornea shape and overall eyeball surface.

Conclusions:

• The automated reconstruction method accurately represents complex eye morphology.
• The ten quantified metrics can parameterize eyeballs across subjects for potential eye disease diagnosis.