Auto Localization and Segmentation of Occluded Vessels in Robot-Assisted Partial Nephrectomy

  • Alborz Amir-Khalili
  • Jean-Marc Peyrat
  • Julien Abinahed
  • Osama Al-Alao
  • Abdulla Al-Ansari
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Hilar dissection is an important and delicate stage in partial nephrectomy during which surgeons remove connective tissue surrounding renal vasculature. Potentially serious complications arise when vessels occluded by fat are missed in the endoscopic view and are not appropriately clamped. To aid in vessel discovery, we propose an automatic method to localize and label occluded vasculature. Our segmentation technique is adapted from phase-based video magnification, in which we measure subtle motion from periodic changes in local phase information albeit for labeling rather than magnification. We measure local phase through spatial decomposition of each frame of the endoscopic video using complex wavelet pairs. We then assign segmentation labels based on identifying responses of regions exhibiting temporal local phase changes matching the heart rate frequency. Our method is evaluated with a retrospective study of eight real robot-assisted partial nephrectomies demonstrating utility for surgical guidance that could potentially reduce operation times and complication rates.


Receiver Operating Characteristic Partial Nephrectomy Occlude Vessel Local Phase Laparoscopic Partial Nephrectomy 
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  1. 1.
    Drucker, B.J.: Renal cell carcinoma: current status and future prospects. Cancer Treatment Reviews 31(7), 536–545 (2005)CrossRefGoogle Scholar
  2. 2.
    Gill, I.S., et al.: Laparoscopic partial nephrectomy for renal tumor: Duplicating open surgical techniques. The Journal of Urology 167(2, Part 1), 469–476 (2002)CrossRefGoogle Scholar
  3. 3.
    Singh, I.: Robot-assisted laparoscopic partial nephrectomy: Current review of the technique and literature. Journal of Minimal Access Surgery 5(4), 87 (2009)CrossRefGoogle Scholar
  4. 4.
    Ramani, A.P., Desai, M.M., Steinberg, A.P., Ng, C.S., Abreu, S.C., Kaouk, J.H., Finelli, A., Novick, A.C., Gill, I.S.: Complications of laparoscopic partial nephrectomy in 200 cases. The Journal of Urology 173(1), 42–47 (2005)CrossRefGoogle Scholar
  5. 5.
    Urban, B.A., Ratner, L.E., Fishman, E.K.: Three-dimensional volume-rendered CT angiography of the renal arteries and veins: Normal anatomy, variants, and clinical applications. RadioGraphics 21(2), 373–386 (2001)CrossRefGoogle Scholar
  6. 6.
    Sampaio, F., Passos, M.: Renal arteries: anatomic study for surgical and radiological practice. Surgical and Radiologic Anatomy 14(2), 113–117 (1992)CrossRefGoogle Scholar
  7. 7.
    Mottrie, A., De Naeyer, G., Schatteman, P., et al.: Impact of the learning curve on perioperative outcomes in patients who underwent robotic partial nephrectomy for parenchymal renal tumours. European Urology 58(1), 127–133 (2010)CrossRefGoogle Scholar
  8. 8.
    Teber, D., Guven, S., Simpfendörfer, T., Baumhauer, M., Güven, E.O., Yencilek, F., Gözen, A.S., Rassweiler, J.: Augmented reality: a new tool to improve surgical accuracy during laparoscopic partial nephrectomy? Preliminary in vitro and in vivo results. European Urology 56(2), 332–338 (2009)CrossRefGoogle Scholar
  9. 9.
    Tobis, S., Knopf, J., Silvers, C., Yao, J., et al.: Near infrared fluorescence imaging with robotic assisted laparoscopic partial nephrectomy: initial clinical experience for renal cortical tumors. The Journal of Urology 186(1), 47–52 (2011)CrossRefGoogle Scholar
  10. 10.
    Crane, N.J., Gillern, S.M., Tajkarimi, K., Levin, I.W., Pinto, P.A., et al.: Visual enhancement of laparoscopic partial nephrectomy with 3-charge coupled device camera: assessing intraoperative tissue perfusion and vascular anatomy by visible hemoglobin spectral response. The Journal of Urology 184(4), 1279–1285 (2010)CrossRefGoogle Scholar
  11. 11.
    Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.T.: Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics 31(4), 65 (2012)CrossRefGoogle Scholar
  12. 12.
    Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Phase-based video motion processing. ACM Transactions on Graphics 32(4), 80 (2013)CrossRefGoogle Scholar
  13. 13.
    McLeod, A.J., Baxter, J.S., de Ribaupierre, S., Peters, T.M.: Motion magnification for endoscopic surgery. In: SPIE: Medical Imaging, vol. 9036, pp. 9036–9011 (2014)Google Scholar
  14. 14.
    Liu, C.: Beyond pixels: exploring new representations and applications for motion analysis. PhD thesis, Massachusetts Institute of Technology (2009)Google Scholar
  15. 15.
    Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. IJCV 40(1), 49–70 (2000)CrossRefzbMATHGoogle Scholar
  16. 16.
    Yushkevich, P.A., Piven, J., Cody Hazlett, H., Gimpel Smith, R., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alborz Amir-Khalili
    • 1
  • Jean-Marc Peyrat
    • 2
  • Julien Abinahed
    • 2
  • Osama Al-Alao
    • 3
  • Abdulla Al-Ansari
    • 2
    • 3
  • Ghassan Hamarneh
    • 4
  • Rafeef Abugharbieh
    • 1
  1. 1.BiSICLUniversity of British ColumbiaVancouverCanada
  2. 2.Qatar Robotic Surgery CentreQatar Science & Technology ParkDohaQatar
  3. 3.Urology DepartmentHamad General Hospital, Hamad Medical CorporationQatar
  4. 4.Medical Image Analysis LabSimon Fraser UniversityBurnabyCanada

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