Braun Daniel, Yang Sungwook, Martel Joseph N, Riviere Cameron N, Becker Brian C
The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Int J Med Robot. 2018 Feb;14(1). doi: 10.1002/rcs.1848. Epub 2017 Jul 18.
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.
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.
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).
EyeSLAM robustly provides retinal maps and registration that enable intelligent surgical micromanipulators to aid surgeons in simulated retinal vessel tracing and photocoagulation tasks.
视网膜血管系统的快速、精确映射和定位对于提高机器人辅助眼内显微手术（如激光光凝和视网膜血管插管）的有效性和临床实用性至关重要。
所提出的EyeSLAM算法在眼内手术期间以30赫兹的频率实时同步定位和映射人眼视网膜及血管系统，将快速血管检测与二维扫描匹配技术相结合，以构建和定位血管系统的概率地图。
在高放大倍数、快速抖动、视网膜背景无纹理、光照变化和工具遮挡等恶劣的视网膜手术成像环境中，EyeSLAM能够在初始化两秒内映射75%的血管，并以低于5.0像素（平移）和1°（旋转）的均方根误差实时定位视网膜。
EyeSLAM能够稳健地提供视网膜地图和配准，使智能手术微操作器能够在模拟视网膜血管追踪和光凝任务中辅助外科医生。
