Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design

• URL: http://arxiv.org/abs/2504.18549v1
• Date: Sun, 13 Apr 2025 05:35:17 GMT
• Title: Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design
• Authors: Boyuan Peng, Jiaju Chen, Yiwei Zhang, Cuiyi Peng, Junyang Li, Jiaming Deng, Peiwu Qin,
• Abstract summary: This study presents a compact, dual-function optical device that integrates fundus photography and refractive error detection into a unified platform.<n>The proposed framework offers a promising solution for rapid, intelligent, and scalable ophthalmic screening, particularly suitable for community health settings.
• Score: 6.979642495062275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing burden of myopia and retinal diseases necessitates more accessible and efficient eye screening solutions. This study presents a compact, dual-function optical device that integrates fundus photography and refractive error detection into a unified platform. The system features a coaxial optical design using dichroic mirrors to separate wavelength-dependent imaging paths, enabling simultaneous alignment of fundus and refraction modules. A Dense-U-Net-based algorithm with customized loss functions is employed for accurate pupil segmentation, facilitating automated alignment and focusing. Experimental evaluations demonstrate the system's capability to achieve high-precision pupil localization (EDE = 2.8 px, mIoU = 0.931) and reliable refractive estimation with a mean absolute error below 5%. Despite limitations due to commercial lens components, the proposed framework offers a promising solution for rapid, intelligent, and scalable ophthalmic screening, particularly suitable for community health settings.

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