The effect of depth perception on visual-motor compensation in minimal invasive surgery

  • Authors:
  • Marios Nicolaou;Louis Atallah;Adam James;Julian Leong;Ara Darzi;Guang-Zhong Yang

  • Affiliations:
  • Royal Society/Wolfson Medical Image Computing Laboratory & Department of Surgical Oncology and Technology, Imperial College London, London, UK;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Surgical Oncology and Technology, Imperial College London, London, UK;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Surgical Oncology and Technology, Imperial College London, London, UK;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Surgical Oncology and Technology, Imperial College London, London, UK;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Surgical Oncology and Technology, Imperial College London, London, UK;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Surgical Oncology and Technology, Imperial College London, London, UK

  • Venue:
  • Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
  • Year:
  • 2006

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Abstract

Despite technological advances in minimally invasive surgery (MIS) in recent years, 3D visualization of the operative field still remains one of greatest challenges. In this paper, the effect of three visualization techniques including conventional 2D, 2D with enhanced depth cue based on shadow, and active 3D displays for novices with no prior adaptation to laparoscopic visualization techniques has been analyzed. A wavelet based paradigm is proposed which offer important insights into the effect of depth perception and visual-motor compensation when performing MIS instrument maneuvers. The proposed method has shown to be advantageous over conventional end-point methods of laparoscopic performance assessment as important supplementary information can be derived from the same trajectories where conventional measures fail to show significant differences.