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Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

  • Veronica PenzaEmail author
  • Jesús Ortiz
  • Leonardo S. Mattos
  • Antonello Forgione
  • Elena De Momi
Original Article

Abstract

Purpose

Single-incision laparoscopic surgery decreases postoperative infections, but introduces limitations in the surgeon’s maneuverability and in the surgical field of view. This work aims at enhancing intra-operative surgical visualization by exploiting the 3D information about the surgical site. An interactive guidance system is proposed wherein the pose of preoperative tissue models is updated online. A critical process involves the intra-operative acquisition of tissue surfaces. It can be achieved using stereoscopic imaging and 3D reconstruction techniques. This work contributes to this process by proposing new methods for improved dense 3D reconstruction of soft tissues, which allows a more accurate deformation identification and facilitates the registration process.

Methods

Two methods for soft tissue 3D reconstruction are proposed: Method 1 follows the traditional approach of the block matching algorithm. Method 2 performs a nonparametric modified census transform to be more robust to illumination variation. The simple linear iterative clustering (SLIC) super-pixel algorithm is exploited for disparity refinement by filling holes in the disparity images.

Results

The methods were validated using two video datasets from the Hamlyn Centre, achieving an accuracy of 2.95 and 1.66 mm, respectively. A comparison with ground-truth data demonstrated the disparity refinement procedure: (1) increases the number of reconstructed points by up to 43 % and (2) does not affect the accuracy of the 3D reconstructions significantly.

Conclusion

Both methods give results that compare favorably with the state-of-the-art methods. The computational time constraints their applicability in real time, but can be greatly improved by using a GPU implementation.

Keywords

Surface reconstruction Super-pixel segmentation Robotic surgery Census transform Depth estimation 

Notes

Acknowledgments

Authors would like to thank Nikhil Deshpande for his kind suggestions and support during the elaboration of this paper.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

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Copyright information

© CARS 2015

Authors and Affiliations

  • Veronica Penza
    • 1
    • 5
    Email author
  • Jesús Ortiz
    • 1
  • Leonardo S. Mattos
    • 1
  • Antonello Forgione
    • 2
    • 3
    • 4
  • Elena De Momi
    • 5
  1. 1.Department of Advanced RoboticsIstituto Italiano di TecnologiaGenoaItaly
  2. 2.Ospedale Niguarda Ca’ GrandaMilanItaly
  3. 3.AIMS AcademyMilanItaly
  4. 4.Valuebiotech s.r.l.MilanItaly
  5. 5.Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanItaly

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