Shohin Mukherjee1, Michael Kaess1, Joseph N Martel2, Cameron N Riviere3. 1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. 2. Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, 15213, USA. 3. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. camr@ri.cmu.edu.
Abstract
PURPOSE: Robot-assisted intraocular microsurgery can improve performance by aiding the surgeon in operating on delicate micron-scale anatomical structures of the eye. In order to account for the eyeball motion that is typical in intraocular surgery, there is a need for fast and accurate algorithms that map the retinal vasculature and localize the retina with respect to the microscope. METHODS: This work extends our previous work by a graph-based SLAM formulation using a sparse incremental smoothing and mapping (iSAM) algorithm. RESULTS: The resulting technique, "EyeSAM," performs SLAM for intraoperative vitreoretinal surgical use while avoiding spurious duplication of structures as with the previous simpler technique. The technique also yields reduction in average pixel error in the camera motion estimation. CONCLUSIONS: This work provides techniques to improve intraoperative tracking of retinal vasculature by handling loop closures and achieving increased robustness to quick shaky motions and drift due to uncertainties in the motion estimation.
PURPOSE: Robot-assisted intraocular microsurgery can improve performance by aiding the surgeon in operating on delicate micron-scale anatomical structures of the eye. In order to account for the eyeball motion that is typical in intraocular surgery, there is a need for fast and accurate algorithms that map the retinal vasculature and localize the retina with respect to the microscope. METHODS: This work extends our previous work by a graph-based SLAM formulation using a sparse incremental smoothing and mapping (iSAM) algorithm. RESULTS: The resulting technique, "EyeSAM," performs SLAM for intraoperative vitreoretinal surgical use while avoiding spurious duplication of structures as with the previous simpler technique. The technique also yields reduction in average pixel error in the camera motion estimation. CONCLUSIONS: This work provides techniques to improve intraoperative tracking of retinal vasculature by handling loop closures and achieving increased robustness to quick shaky motions and drift due to uncertainties in the motion estimation.
Authors: Daniel Braun; Sungwook Yang; Joseph N Martel; Cameron N Riviere; Brian C Becker Journal: Int J Med Robot Date: 2017-07-18 Impact factor: 2.547
Authors: Sophie Rogers; Rachel L McIntosh; Ning Cheung; Lyndell Lim; Jie Jin Wang; Paul Mitchell; Jonathan W Kowalski; Hiep Nguyen; Tien Y Wong Journal: Ophthalmology Date: 2010-02 Impact factor: 12.079
Authors: Ali Uneri; Marcin A Balicki; James Handa; Peter Gehlbach; Russell H Taylor; Iulian Iordachita Journal: Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron Date: 2010-09-01