EyeSLAM: Real-time localization and mapping of retinal vessels during intraocular microsurgery
Abstract
BACKGROUND:
Fast and accurate mapping and localization of the retinal vasculature is critical to increasing the effectiveness and clinical utility of robot-assisted intraocular microsurgery such as laser photocoagulation and retinal vessel cannulation.
METHODS:
The proposed EyeSLAM algorithm delivers 30 Hz real-time simultaneous localization and mapping of the human retina and vasculature during intraocular surgery, combining fast vessel detection with 2D scan-matching techniques to build and localize a probabilistic map of the vasculature.
RESULTS:
In the harsh imaging environment of retinal surgery with high magnification, quick shaky motions, textureless retina background, variable lighting and tool occlusion, EyeSLAM can map 75% of the vessels within two seconds of initialization and localize the retina in real time with a root mean squared (RMS) error of under 5.0 pixels (translation) and 1° (rotation).
CONCLUSIONS:
EyeSLAM robustly provides retinal maps and registration that enable intelligent surgical micromanipulators to aid surgeons in simulated retinal vessel tracing and photocoagulation tasks.
BibTeX
@article{Riviere-2018-106368,author = {Daniel Braun and Sungwook Yang and Joseph N. Martel and Cameron N. Riviere and Brian C. Becker},
title = {EyeSLAM: Real-time localization and mapping of retinal vessels during intraocular microsurgery},
journal = {International Journal of Medical Robotics and Computer Assisted Surgery},
year = {2018},
month = {February},
volume = {14},
number = {1},
pages = {e1848},
keywords = {intraocular microsurgery; robotic micromanipulation; vessel detection; medical robotics},
}