3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations
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| Format: | Preprint |
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2025
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| author | Sharma, Swati Braeu, Fabian A. Chuangsuwanich, Thanadet Tun, Tin A. Hoang, Quan V Chong, Rachel Perera, Shamira Ho, Ching-Lin Husain, Rahat Buist, Martin L. Aung, Tin Girard, Michaël J. A. |
| author_facet | Sharma, Swati Braeu, Fabian A. Chuangsuwanich, Thanadet Tun, Tin A. Hoang, Quan V Chong, Rachel Perera, Shamira Ho, Ching-Lin Husain, Rahat Buist, Martin L. Aung, Tin Girard, Michaël J. A. |
| contents | Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma, and to evaluate their variations across these conditions. Participants: A total of 685 optical coherence tomography (OCT) scans from 754 subjects of Singapore-Chinese ethnicity, including 256 healthy (H), 94 highly myopic (HM), 227 glaucomatous (G), and 108 highly myopic with glaucoma (HMG) cases. Methods: We segmented the retinal and connective tissues from OCT volumes and their boundary edges were converted into 3D point clouds. To classify the 3D point clouds into four ONH conditions, i.e., H, HM, G, and HMG, a specialized ensemble network was developed, consisting of an encoder to transform high-dimensional input data into a compressed latent vector, a decoder to reconstruct point clouds from the latent vector, and a classifier to categorize the point clouds into the four ONH conditions. Results: The classification network achieved high accuracy, distinguishing H, HM, G, and HMG classes with a micro-average AUC of 0.92 $\pm$ 0.03 on an independent test set. The decoder effectively reconstructed point clouds, achieving a Chamfer loss of 0.013 $\pm$ 0.002. Dimensionality reduction clustered ONHs into four distinct groups, revealing structural variations such as changes in retinal and connective tissue thickness, tilting and stretching of the disc and scleral canal opening, and alterations in optic cup morphology, including shallow or deep excavation, across the four conditions. Conclusions: This study demonstrated that ONHs exhibit distinct structural signatures across H, HM, G, and HMG conditions. The findings further indicate that ONH morphology provides sufficient information for classification into distinct clusters, with principal components capturing unique structural patterns within each group. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19083 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations Sharma, Swati Braeu, Fabian A. Chuangsuwanich, Thanadet Tun, Tin A. Hoang, Quan V Chong, Rachel Perera, Shamira Ho, Ching-Lin Husain, Rahat Buist, Martin L. Aung, Tin Girard, Michaël J. A. Image and Video Processing Computer Vision and Pattern Recognition Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma, and to evaluate their variations across these conditions. Participants: A total of 685 optical coherence tomography (OCT) scans from 754 subjects of Singapore-Chinese ethnicity, including 256 healthy (H), 94 highly myopic (HM), 227 glaucomatous (G), and 108 highly myopic with glaucoma (HMG) cases. Methods: We segmented the retinal and connective tissues from OCT volumes and their boundary edges were converted into 3D point clouds. To classify the 3D point clouds into four ONH conditions, i.e., H, HM, G, and HMG, a specialized ensemble network was developed, consisting of an encoder to transform high-dimensional input data into a compressed latent vector, a decoder to reconstruct point clouds from the latent vector, and a classifier to categorize the point clouds into the four ONH conditions. Results: The classification network achieved high accuracy, distinguishing H, HM, G, and HMG classes with a micro-average AUC of 0.92 $\pm$ 0.03 on an independent test set. The decoder effectively reconstructed point clouds, achieving a Chamfer loss of 0.013 $\pm$ 0.002. Dimensionality reduction clustered ONHs into four distinct groups, revealing structural variations such as changes in retinal and connective tissue thickness, tilting and stretching of the disc and scleral canal opening, and alterations in optic cup morphology, including shallow or deep excavation, across the four conditions. Conclusions: This study demonstrated that ONHs exhibit distinct structural signatures across H, HM, G, and HMG conditions. The findings further indicate that ONH morphology provides sufficient information for classification into distinct clusters, with principal components capturing unique structural patterns within each group. |
| title | 3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.19083 |