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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.18549 |
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| _version_ | 1866917999657615360 |
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| author | Peng, Boyuan Chen, Jiaju Zhang, Yiwei Peng, Cuiyi Li, Junyang Deng, Jiaming Qin, Peiwu |
| author_facet | Peng, Boyuan Chen, Jiaju Zhang, Yiwei Peng, Cuiyi Li, Junyang Deng, Jiaming Qin, Peiwu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18549 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design Peng, Boyuan Chen, Jiaju Zhang, Yiwei Peng, Cuiyi Li, Junyang Deng, Jiaming Qin, Peiwu Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.18549 |