Fast Semi-dense Surface Reconstruction from Stereoscopic Video in Laparoscopic Surgery

  • Johannes Totz
  • Stephen Thompson
  • Danail Stoyanov
  • Kurinchi Gurusamy
  • Brian R. Davidson
  • David J. Hawkes
  • Matthew J. Clarkson
Conference paper

DOI: 10.1007/978-3-319-07521-1_22

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8498)
Cite this paper as:
Totz J. et al. (2014) Fast Semi-dense Surface Reconstruction from Stereoscopic Video in Laparoscopic Surgery. In: Stoyanov D., Collins D.L., Sakuma I., Abolmaesumi P., Jannin P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2014. Lecture Notes in Computer Science, vol 8498. Springer, Cham

Abstract

Liver resection is the main curative option for liver metastases. While this offers a 5-year survival rate of 50%, only about 20% of all patients are suitable for laparoscopic resection and thus being able to take advantage of minimally invasive surgery. One underlying difficulty is the establishment of a safe resection margin while avoiding critical structures. Intra-operative registration of patient scan data may provide a solution. However, this relies on fast and accurate reconstruction methods to obtain the current shape of the liver. Therefore, this paper presents a method for high-resolution stereoscopic surface reconstruction at interactive rates. To this end, a feature-matching propagation method is adapted to multi-resolution processing to enable parallelisation, remove global synchronisation issues and hence become amenable to a GPU-based implementation. Experiments are conducted on a planar target for reconstruction noise estimation and a visually realistic silicone liver phantom. Results highlight an average reconstruction error of 0.6 mm on the planar target, 2.4–5.7 mm on the phantom and processing times averaging around 370 milliseconds for input images of size 1920 x 540.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Johannes Totz
    • 1
  • Stephen Thompson
    • 1
  • Danail Stoyanov
    • 1
  • Kurinchi Gurusamy
    • 2
  • Brian R. Davidson
    • 2
  • David J. Hawkes
    • 1
  • Matthew J. Clarkson
    • 1
  1. 1.Centre for Medical Image ComputingUniversity College LondonUK
  2. 2.Royal Free HospitalLondonUK

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