一款可同时拍眼底和测近视的智能眼科设备，适合社区筛查。
Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design
- 用同轴光路分波长成像，眼底与验光模块同步对焦。
- 瞳孔定位误差仅2.8像素，平均交并比达0.931，验光误差低于5%。
- 轻量化设计适合基层医疗，自动化程度高，部署便捷。
近视和视网膜疾病负担日益加重，亟需更便捷高效的视力筛查方案。本研究提出一种紧凑型双功能光学装置，将眼底摄影与屈光不正检测集成于同一平台。系统采用分色镜实现同轴光学设计，分离波长依赖性成像路径，实现眼底与验光模块的同步对准。基于Dense-U-Net的算法结合定制损失函数，实现精准瞳孔分割，支持自动对焦与定位。实验验证表明，该系统在瞳孔定位上达到2.8像素（EDE）的高精度，平均交并比（mIoU）为0.931；屈光度估计均方误差低于5%。尽管受限于商用镜头组件，该框架仍展现出快速、智能且可扩展的眼科筛查潜力，尤其适用于社区健康场景。
原文摘要 · Abstract (English)
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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