Instrument contact force estimation using endoscopic image sequence and 3D reconstruction model

This paper presents a method to estimate contact force for minimally invasive surgery (MIS) using endoscopic imagery. In order to provide surgeons the information of contact force as surgical instruments get contact to human tissue, we developed a method to calculate tissue deformation by reconstructing 3D model using real-time image processing. The force information will be fed back to a haptic device to allow the surgeon to feel contact force as well as to show the force value on monitor for the surgeon. We propose a 3D reconstruction method to model the tissue surface under deformation, and use the deformation data to obtain contact force near the instrument tip. Using image tracking, we locate a surgical instrument in the endoscopy scene, then determine the deformation area in the image scene. Several experiments validate the effectiveness of method of deformation calculation.

[1]  Tobias Ortmaier,et al.  Design requirements for a new robot for minimally invasive surgery , 2004, Ind. Robot.

[2]  R. V. Patel,et al.  Effect of force feedback on performance of robotics-assisted suturing , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[3]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[4]  S. P. Mudur,et al.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[5]  Kai-Tai Song,et al.  Image tracking of laparoscopic instrument using spiking neural networks , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[6]  Soo Jay Phee,et al.  Force feedback without sensor: A preliminary study on haptic modeling , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[7]  Kaspar Althoefer,et al.  A novel tumor localization method using haptic palpation based on soft tissue probing data , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Ling Zhu,et al.  A real-time deformation modeling scheme of soft tissue for virtual surgical , 2010, The 2010 IEEE International Conference on Information and Automation.

[9]  Imin Kao,et al.  Noncontact Active Sensing for Viscoelastic Parameters of Tissue With Coupling Effect , 2011, IEEE Transactions on Biomedical Engineering.

[10]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[12]  Yung-Nien Sun,et al.  Automatic extraction and visualization of human inner structures from endoscopic image sequences , 2004, SPIE Medical Imaging.

[13]  Peter Kazanzides,et al.  Safety design for medical robots , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Philippe Poignet,et al.  Medically safe and sound [human-friendly robot dependability] , 2004, IEEE Robotics & Automation Magazine.

[15]  Guang-Zhong Yang,et al.  Deformable structure from motion by fusing visual and inertial measurement data , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.