Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design
Journal:
arXiv
Published Date:
Apr 13, 2025
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.
