International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2014: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 pp 324-331

Efficient Multi-organ Segmentation in Multi-view Endoscopic Videos Using Pre-operative Priors

  • Masoud S. Nosrati
  • Jean-Marc Peyrat
  • Julien Abinahed
  • Osama Al-Alao
  • Abdulla Al-Ansari
  • Rafeef Abugharbieh
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Synergistic fusion of pre-operative (pre-op) and intra- operative (intra-op) imaging data provides surgeons with invaluable insightful information that can improve their decision-making during minimally invasive robotic surgery. In this paper, we propose an efficient technique to segment multiple objects in intra-op multi-view endoscopic videos based on priors captured from pre-op data. Our approach leverages information from 3D pre-op data into the analysis of visual cues in the 2D intra-op data by formulating the problem as one of finding the 3D pose and non-rigid deformations of tissue models driven by features from 2D images. We present a closed-form solution for our formulation and demonstrate how it allows for the inclusion of laparoscopic camera motion model. Our efficient method runs in real-time on a single core CPU making it practical even for robotic surgery systems with limited computational resources. We validate the utility of our technique on ex vivo data as well as in vivo clinical data from laparoscopic partial nephrectomy surgery and demonstrate its robustness in segmenting stereo endoscopic videos.

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References

  1. 1.
    Bai, X., et al.: Video SnapCut: robust video object cutout using localized classifiers. ACM Trans. Graphics 28(3), 70:1–70:11 (2009)Google Scholar
  2. 2.
    Dhandra, B., et al.: Analysis of abnormality in endoscopic images using combined HSI color space and watershed segmentation. In: ICPR, vol. 4, pp. 695–698 (2006)Google Scholar
  3. 3.
    Estépar, R., et al.: Multimodality guidance in endoscopic and laparoscopic abdominal procedures. In: Intraop. Imag. Image-Guided Therapy, pp. 767–778 (2014)Google Scholar
  4. 4.
    Figueiredo, I., et al.: Variational image segmentation for endoscopic human colonic aberrant crypt foci. IEEE TMI 29(4), 998–1011 (2010)Google Scholar
  5. 5.
    Figueiredo, I., et al.: A segmentation model and application to endoscopic images. In: Image Anal. Recogn., pp. 164–171 (2012)Google Scholar
  6. 6.
    Hamarneh, G., Jassi, P., Tang, L.: Simulation of ground-truth validation data via physically- and statistically-based warps. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 459–467. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Mewes, P.W., Neumann, D., Licegevic, O., Simon, J., Juloski, A.L., Angelopoulou, E.: Automatic region-of-interest segmentation and pathology detection in magnetically guided capsule endoscopy. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 141–148. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Mountney, P., Yang, G.-Z.: Motion compensated SLAM for image guided surgery. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A., et al. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 496–504. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Pratt, P., et al.: An effective visualisation and registration system for image-guided robotic partial nephrectomy. J. Robotic Surg. 6(1), 23–31 (2012)CrossRefGoogle Scholar
  10. 10.
    Prisacariu, V., Reid, I.: PWP3D: Real-time segmentation and tracking of 3d objects. IJCV 98(3), 335–354 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Puerto-Souza, G., Mariottini, G.: Toward long-term and accurate augmented-reality display for minimally-invasive surgery. In: ICRA, pp. 5384–5389 (2013)Google Scholar
  12. 12.
    Sandhu, R., et al.: A nonrigid kernel-based framework for 2D-3D pose estimation and 2D image segmentation. IEEE TPAMI 33(6), 1098–1115 (2011)CrossRefGoogle Scholar
  13. 13.
    Top, A., Hamarneh, G., Abugharbieh, R.: Active learning for interactive 3D image segmentation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 603–610. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. IJCV 50(3), 271–293 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Masoud S. Nosrati
    • 1
  • Jean-Marc Peyrat
    • 2
  • Julien Abinahed
    • 2
  • Osama Al-Alao
    • 3
  • Abdulla Al-Ansari
    • 2
    • 3
  • Rafeef Abugharbieh
    • 4
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis LabSimon Fraser UniversityCanada
  2. 2.Qatar Robotic Surgery CentreQatar Science & Technology ParkQatar
  3. 3.Urology Department, Hamad General HospitalHamad Medical CorporationQatar
  4. 4.BiSICLUniversity of British ColumbiaCanada

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