Tissue Deformation Recovery with Gaussian Mixture Model Based Structure from Motion

  • Stamatia Giannarou
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7264)


Accurate 3D reconstruction of the surgical scene is important in intra-operative guidance. Existing methods are often based on the assumption that the camera is static or the tissue is deforming with periodic motion. In minimally invasive surgery, these assumptions do not always hold due to free-form tissue deformation induced by instrument-tissue interaction and camera motion required for continuous exploration of the surgical scene, particularly for intraluminal procedures. The aim of this work is to propose a novel framework for intra-operative free-form deformation recovery. The proposed method builds on a compact scene representation scheme that is suitable for both surgical episode identification and instrument-tissue motion modeling. Unlike previous approaches, it does not impose explicit models on tissue deformation, allowing realistic free-form deformation recovery. Validation is provided on both synthetic and phantom data. The practical value of the method is further demonstrated by deformation recovery on in vivo data recorded from a robotic assisted minimally invasive surgical procedure.


Gaussian Mixture Model Minimally Invasive Surgery Camera Motion Tissue Deformation Structure From Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stamatia Giannarou
    • 1
  • Guang-Zhong Yang
    • 1
  1. 1.Hamlyn Centre for Robotic SurgeryImperial CollegeLondonUK

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