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Demo Abstract: Edge-based Augmented Reality Guidance System for Retinal Laser Therapy via Feature Matching

Published: 09 May 2023 Publication History

Abstract

In ophthalmology, retinal laser therapy is a treatment for retinopathy that requires the use of magnifying lens to treat damaged regions of retinal landmarks, hence creating challenges of inverted magnified images and requiring prolonged training. Augmented Reality (AR) can benefit clinicians during retinal laser therapy by guiding them with retinal landmark holograms and contextual information. Though recent developments in AR magnification show that a direct overlay of the magnified scenes can be achieved, retinal laser therapy requires high precision and visual acuity while maintaining the visual perception of the rest of the environment. Therefore, we demonstrate an AR-based selective magnification system that provides contextual and visualization-based guidance to clinicians. An edge-computing architecture is developed for detecting and matching the feature points between the magnified image and color fundus image of the retina to identify the magnified region of retinal landmarks. We showcase how our AR guidance system can assist clinicians during retinal laser therapy.

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References

[1]
Kittipat Apicharttrisorn, Xukan Ran, Jiasi Chen, Srikanth V Krishnamurthy, and Amit K Roy-Chowdhury. 2019. Frugal following: Power thrifty object detection and tracking for mobile augmented reality. In Proc. ACM SenSys.
[2]
Zubin Choudhary, Jesus Ugarte, Gerd Bruder, and Greg Welch. 2021. Real-Time Magnification in Augmented Reality. In Proc. ACM SUI.
[3]
Lesley A Everett and Yannis M Paulus. 2021. Laser therapy in the treatment of diabetic retinopathy and diabetic macular edema. Current Diabetes Reports 21, 9 (2021), 1–12.
[4]
Long Qian, Tianyu Song, Mathias Unberath, and Peter Kazanzides. 2022. AR-Loupe: Magnified augmented reality by combining an optical see-through head-mounted display and a loupe. IEEE Transactions on Visualization and Computer Graphics 28, 7 (2022), 2550–2562.
[5]
Haoxin Wang, BaekGyu Kim, Jiang Linda Xie, and Zhu Han. 2022. LEAF+ AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality. IEEE Transactions on Mobile Computing (2022).

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  1. Demo Abstract: Edge-based Augmented Reality Guidance System for Retinal Laser Therapy via Feature Matching

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      cover image ACM Conferences
      IPSN '23: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks
      May 2023
      385 pages
      ISBN:9798400701184
      DOI:10.1145/3583120
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Publication History

      Published: 09 May 2023

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      Author Tags

      1. Augmented reality
      2. edge computing
      3. feature matching
      4. magnification
      5. retinal laser therapy

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