June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Low-Cost Raspberry Pi Stereoscopic Fundus Imaging System
Author Affiliations & Notes
  • Michael Yan
    Gavin Herbert Eye Institute, Department of Ophthalmology, University of California Irvine, Irvine, California, United States
  • Josiah K To
    Gavin Herbert Eye Institute, Department of Ophthalmology, University of California Irvine, Irvine, California, United States
  • Anderson N. Vu
    Gavin Herbert Eye Institute, Department of Ophthalmology, University of California Irvine, Irvine, California, United States
  • Andrew W Browne
    Gavin Herbert Eye Institute, Department of Ophthalmology, University of California Irvine, Irvine, California, United States
    Institute of Clinical and Translational Science, Department of Biomedical Engineering, University of California Irvine, Irvine, California, United States
  • Footnotes
    Commercial Relationships   Michael Yan, None; Josiah To, None; Anderson Vu, None; Andrew Browne, None
  • Footnotes
    Support  This project was supported by an RPB unrestricted grant to UCI Department of Ophthalmology and UCI ICTS NIH KL2 Grant number is KL2 TR001416.
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2301. doi:
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    • Get Citation

      Michael Yan, Josiah K To, Anderson N. Vu, Andrew W Browne; Low-Cost Raspberry Pi Stereoscopic Fundus Imaging System. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2301.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Funduscopic examination clinical ophthalmology provides a sense of depth for ophthalmologists to better evaluate ocular health and disease progression. Optical coherence tomography can capture three-dimensional anatomy, but is not routinely available in the developing world. Therefore, we designed a cost-effective stereoscopic camera system able to produce optically registered photos of the fundus, which can be used for ophthalmic diagnosis and management in low resource settings.

Methods : Fundoscopic images were taken using the Raspberry Pi (RPi) compute module 3+, RPi stereo camera module, and two 5-megapixel RPi camera modules. Each RPi camera module was encased in a 3D printed custom adapter allowing 6 degrees of freedom (forward/backward, left/right, up/down), attached to a Zeiss 6x18 T monocular, and then connected to a custom 3D printed binocular indirect ophthalmoscope case for the Heine Omega 180 (Fig 1B). The RPi camera modules’ central axes were manually aligned using text and a grid on a sample paper. The fundus was visualized through a Volk Digital Clear Field indirect ophthalmic lens that fit into a custom 3D printed focusing track. Light from the Heine Omega 180 was used for transpupillary illumination of the fundus for image acquisition. The entire image capture system was attached to an existing slit lamp for ease of test administration. All custom 3D printed parts were constructed using Polylactic Acid on the Fused Deposition Modeling 3D printer Prusa MK3. Both video capture and image acquisition were controlled by an in-house graphical user interface written in Python using the OpenCV library.

Results : The tonal quality (brightness, contrast, color balance) of the color retinal photographs demonstrate adequate image quality for use in diagnostic evaluation. The optic nerve, macula, and retinal blood vasculature can be clearly visualized.

Conclusions : We built an economic stereoscopic camera system that can capture clear images of the fundus, which can also be remotely accessed for teleophthalmology. Moreover, the high image quality-to-price ratio, stereoscopic function, and remote access make our camera system sustainable in developing countries.

This is a 2021 ARVO Annual Meeting abstract.

 

Fig 1. A) RPi camera modules encased in a 3D printed custom adapter B) Camera system attached to a standard slit lamp C) GUI used to input commands to cameras

Fig 1. A) RPi camera modules encased in a 3D printed custom adapter B) Camera system attached to a standard slit lamp C) GUI used to input commands to cameras

 

Fig 2. Left eye fundus images taken simultaneously with two 5-megapixel RPi cameras from a parallax of 65 mm apart.

Fig 2. Left eye fundus images taken simultaneously with two 5-megapixel RPi cameras from a parallax of 65 mm apart.

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