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.

Sensing and Augmented Reality (AR) can benefit a wide range of applications that involve the use of magnifying lenses. Recent developments in AR magnification provide a direct overlay of the magnified scenes in AR. However, instrumentation tasks that require high precision and visual acuity need to selectively magnify a region of interest while maintaining the visual perception of the rest of the environment. In this demo, we present AR-Magnifier, an AR magnification system through feature detection and matching. We propose a general framework based on an edge-computing architecture that can be applied to various types of instrumentation tasks. A pipeline is developed for detecting feature points and computing the homography matching to identify the magnified region of an object. We showcase how selective magnification in AR through sensing can assist the user in complex instrumentation tasks by providing visualization-based guidance.