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The Emory Optic Nerve Head Atlas - Using 3D Anatomical Mapping to Study Optic Neuropathies with an Initial Focus on
Thanadet Chuangsuwanich1,2, Patipol Tiyajamorn1,2, Yibo Chen1,2
1Department of Ophthalmology, Emory University School of Medicine, Atlanta, Georgia USA.
Purpose:
To develop the first 3D optic nerve head (ONH) atlas using AI-based registration and evaluate its use in: (1) atlas-adjusted retinal nerve fiber layer (RNFL) analysis for glaucoma diagnosis, and (2) strain-based assessment of glaucoma severity.
Methods:
Large-scale OCT datasets were registered using REFLECTIVITY-generated tissue segmentations. A healthy atlas (n=460) and glaucoma atlases for mild (n=852), moderate (n=640), and severe (n=546) disease were built using an AI-driven algorithm incorporating structural and biomechanical constraints. Atlas quality was evaluated using inter-layer contrast and agreement between warped scans and the template. Atlas-adjusted RNFL thickness was extracted by mapping fixed 1.5 mm Bruch's membrane opening (BMO) based reference points from the atlas to each subject and compared with standard subject-specific RNFL measurements. Effective strain relative to each atlas was computed by morphing subjects to the atlas template. Strain and thickness features were used to classify glaucoma severity.
Results:
The healthy atlas captured normative ONH anatomy, while glaucoma atlases showed stage-dependent changes (cup deepening, prelaminar thinning, rim narrowing). Registration accuracy was high (NCC=0.86±0.05; Dice=0.90±0.02), with strong inter-layer contrast (0.40±0.03). Atlas-adjusted RNFL thickness improved separation between healthy and mild glaucoma (Cohen's d=0.77 vs. 0.72). Classification using atlas-adjusted profiles outperformed native measurements (AUC=0.771 vs. 0.757). Mean effective strain differed significantly across severity groups (ANOVA p<0.05), and CNNs trained on strain maps achieved strong classification (AUC=0.79).
Conclusion:
The ONH atlas provides a population-derived structural reference enabling anatomically consistent thickness mapping and strain-based characterization. It enhances diagnostic discrimination, supports severity classification, and offers a framework to study glaucomatous biomechanics.
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