Deep learning-based scatterer density estimation for the analysis of retinal tissue property

Thitiya Seesan; Shuichi Makita; Masahira Miura; Yoshiaki Yasuno

doi:10.1117/12.3000720

13 March 2024 Deep learning-based scatterer density estimation for the analysis of retinal tissue property

Thitiya Seesan, Shuichi Makita, Masahira Miura, Yoshiaki Yasuno

Proceedings Volume PC12824, Ophthalmic Technologies XXXIV; PC1282411 (2024) https://doi.org/10.1117/12.3000720
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We present deep-learning based multi-contrast optical coherence tomography (OCT) imaging methods for the analysis of retinal tissue properties. Two modalities, synthesizing degree-of-polarization-uniformity (syn-DOPU) and scatterer density estimator (SDE), were introduced. Syn-DOPU generates DOPU images from non-polarization sensitive OCT images, and hence eliminates the need for special hardware. SDE provides robust scatterer density estimation irrespective of measurement and ocular medium conditions. The methods were applied to age-related macular degeneration cases, and revealed the detailed abnormality of the retinal pigment epithelium. Additionally, layer and sector analyses of normal cases demonstrated positional and age-related variations of DOPU and scatterer density.

Conference Presentation

Citation Download Citation

Thitiya Seesan, Shuichi Makita, Masahira Miura, and Yoshiaki Yasuno "Deep learning-based scatterer density estimation for the analysis of retinal tissue property", Proc. SPIE PC12824, Ophthalmic Technologies XXXIV, PC1282411 (13 March 2024); https://doi.org/10.1117/12.3000720

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