83. a ground truth 3d video data set for augmented reality robotic mis algorithms

Department: Computer Science & Engineering
Research Institute Affiliation: Graduate Program in Computational Science, Mathematics, and Engineering (CSME)
Faculty Advisor(s): Ryan Kastner

Primary Student
Name: Michael Joseph Barrow
Email: mbarrow@ucsd.edu
Phone: 858-405-5823
Grad Year: 2018

Abstract
Augmented reality (AR) is an emerging visualization technology that can enhance intraoperative imaging during robotic surgery by superimposing preoperative studies into the camera display. The biggest challenge of this is the lack of accurate modeling for soft tissue in 3 dimensions. Current operative video recordings only capture image feed without measurements of force or tissue surface location (ground truth 3D data), thus limiting the modeling capabilities. The system resolutions are also too low to explore tradeoffs in system accuracy and speed for practical clinical applications. A porcine liver was used as a homogenous soft tissue model. Mechanical manipulation and incision on the liver is performed using a custom hydraulic tool attachment to the DaVinci S Ⓡ robotic surgical system (+/- 0.01mm accuracy). A laser scanner is used to sequentially scan the porcine liver (0.025 mm accuracy up to 1732 dpcm). These scans are pre-registered to a color video feed using an Iterative Closest Point method. Benchmark values for soft tissue in robotic surgery were easily and effectively collected with our recording method. AR developers can now use these values to explore and validate algorithmic, software, and hardware methods for tissue reconstruction.

Industry Application Area(s)
Life Sciences/Medical Devices & Instruments

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