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Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

A practical method for patient-specific modeling of the aortic arch and the entire carotid vasculature from computed tomography angiography (CTA) scans for morphologic analysis and for interventional procedure simulation.

Materials and methods

The method starts with the automatic watershed-based segmentation of the aorta and the construction of an a-priori intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weighting function that adaptively couples voxel intensity, intensity prior, and local vesselness shape prior. Finally, the same graph-cut optimization framework is used to interactively remove a few unwanted veins segments and to fill in minor vessel discontinuities caused by intensity variations.

Results

We validate our modeling method with two experimental studies on 71 multicenter clinical CTA datasets, including carotid bifurcation lumen segmentation on 56 CTAs from the MICCAI’2009 3D Segmentation Challenge. Segmentation results show that our method is comparable to the best existing methods and was successful in modeling the entire carotid vasculature with a Dice similarity measure of 84.5% (SD = 3.3%) and MSSD 0.48 mm (SD = 0.12 mm.) Simulation study shows that patient-specific simulations with four patient-specific models generated by our segmentation method on the ANGIO MentorTM simulator platform are robust, realistic, and greatly improve the simulation.

Conclusion

This constitutes a proof-of-concept that patient-specific CTA-based modeling and simulation of carotid interventional procedures are practical in a clinical environment.

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References

  1. van Bockel JH, Bergqvist D, Cairols M, Liapis CD, Benedetti-Valentini F, Pandey V, Wolfe J, Section E, Vascular Surgery of the European Union of Medical Specialists B (2008) Education in vascular surgery: critical issues around the globe-training and qualification in vascular surgery in europe. J Vasc Surg 48:69S–75S; discussion 75S

    Google Scholar 

  2. Neequaye SK, Aggarwal R, Herzeele IV, Darzi A, Cheshire NJ (2007) Endovascular skills training and assessment. J Vasc Surg 46: 1055–1064

    Article  PubMed  Google Scholar 

  3. Verzini F, Rango PD, Parlani G, Panuccio G, Cao P (2008) Carotid artery stenting: technical issues and role of operators’ experience. Perspect Vasc Surg Endovasc Ther 20: 247–257

    Article  PubMed  Google Scholar 

  4. Simbionix Ltd. Israel (2008) http://www.simbionix.com/angio_mentor.html

  5. Stern J, Zeltser I, Pearle M (2007) Percutaneous renal access simulators. J Endourol 21: 270–273

    Article  PubMed  Google Scholar 

  6. Tedesco MM, Pak JJ, Harris EJ, Krummel TM, Dalman RL, Lee JT (2008) Simulation-based endovascular skills assessment: the future of credentialing? J Vasc Surg 47:1008–1001 (discussion 1014)

    Google Scholar 

  7. Berger P, Willems MCM, Vliet JAVD, Kool LJS, Bergqvist D, Blankensteijn JD (2010) Validation of the simulator for testing and rating endovascular skills (stress)-machine in a setting of competence testing. J Cardiovasc Surg (Torino) 51: 253–256

    CAS  Google Scholar 

  8. Willaert W, Aggarwal R, Nestel D, Gaines P, Vermassen F, Darzi A, Cheshire N (2010) Patient-specific simulation for endovascular procedures: qualitative evaluation of the development process. Int J Med Robot Comput Assist Surg 6: 202–210

    CAS  Google Scholar 

  9. Wu X, Luboz V, Krissian K, Cotin S, Dawson S (2011) Segmentation and reconstruction of vascular structures for 3D real-time simulation. Med Image Anal 15: 22–34

    Article  PubMed  Google Scholar 

  10. Roguin A, Beyar R (2010) Real case virtual reality training prior to carotid artery stenting. Catheter Cardiovasc Interv 75: 279–282

    Article  PubMed  Google Scholar 

  11. Willaert W, Aggarwal R, Bicknell C, Hamady M, Darzi A, Vermassen F, Cheshire N, (EVEResT), E. V. R. E. R. T. (2010) Patient-specific simulation in carotid artery stenting. J Vasc Surg 52:1700–1705

    Google Scholar 

  12. Willaert WI, Aggarwal R, Van Herzeele I, O’Donoghue K, Gaines PA, Darzi AW, Vermassen FE, Cheshire NJ, On behalf of European Virtual Reality Endovascular Research Team EVEResT (2011) Patient-specific endovascular simulation influences interventionalists performing carotid artery stenting procedures. Eur J Vasc Endovasc Surg 41:492–500

    Google Scholar 

  13. Auricchio F, Conti M, Beule MD, Santis GD, Verhegghe B (2011) Carotid artery stenting simulation: from patient-specific images to finite element analysis. Med Eng Phys 33: 281–289

    Article  PubMed  CAS  Google Scholar 

  14. Manniesing R, Viergever M, Niessen W (2007) Vessel axis tracking using topology constrained surface evolution. IEEE Trans Med Imaging 26: 309–316

    Article  PubMed  Google Scholar 

  15. Johnson MH, Thorisson HM, DiLuna ML (2009) Vascular anatomy: the head, neck, and skull base. Neurosurg Clin N Am 20: 239–258

    Article  PubMed  Google Scholar 

  16. Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms. ACM Comput Surv 36: 81–121

    Article  Google Scholar 

  17. Lesage D, Angelini E, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13: 819–845

    Article  PubMed  Google Scholar 

  18. Kim D, Park J (2005) Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images. Image Vis Comput 23: 1277–1287

    Article  Google Scholar 

  19. Frangi A, Niessen W, Vincken K, Viergever M (1998) Multiscale vessel enhancement filtering. In: Proceedings of the 1st international conference on medical image computing and computer assisted interventions, MICCAI’98, vol 1496 of LNCS, pp 130–137

  20. Niessen W, van Bemmel C, Frangi A, Siers M, Wink O (2002) Model-based segmentation of cardiac and vascular images. In: IEEE international symposium on biomedical imaging, ISBI’02, pp 22–25

  21. Lorigo L, Faugeras O, Grimson W, Keriven R, Kikinis R, Nabavi A, Westin C (2001) CURVES: curve evolution for vessel segmentation. Med Image Anal 5: 195–206

    Article  PubMed  CAS  Google Scholar 

  22. Nain D, Yezzi A, Turk G (2004) Vessel segmentation using a shape driven flow. In: Proceedings of the 7th international conference on medical image computing and computer assisted interventions, MICCAI’04, vol 3216 of LNCS, pp 51–59

  23. Scherl H, Hornegger J, Prümmer M, Lell M (2007) Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets. Med Image Anal 11: 21–34

    Article  PubMed  Google Scholar 

  24. Tang H et al (2010) A semi-automatic method for segmentation of the carotid bifurcation and bifurcation angle quantification on black blood MRA. Med Image Comput Comput Assist Interv 13: 97–104

    PubMed  Google Scholar 

  25. Manniesing R, Schaap M, Rozie S, Hameeteman R, Vukadinovic D, van der Lugt A, Niessen W (2010) Robust CTA lumen segmentation of the atherosclerotic carotid artery bifurcation in a large patient population. Med Image Anal 14: 759–769

    Article  PubMed  Google Scholar 

  26. Lekadir K, Merrifield R, Guang-Zhong Y (2007) Outlier detection and handling for robust 3-D active shape models search. IEEE Trans Med Imaging 26: 212–222

    Article  PubMed  Google Scholar 

  27. Schaap M, Manniesing R, Smal T, van Walsum I, van der Lugt A, Niessen W (2007) Bayesian tracking of tubular structures and its application to carotid arteries in CTA. In: Proceedings of the 10th international conference on medical image computing and computer assisted interventions, MICCAI’07, vol 4792 of LNCS, pp 562–570

  28. Friman O, Hindennach M, Kuhnel C, Peitgen H-O (2010) Multiple hypothesis template tracking of small 3D vessel structures. Med Image Anal 14: 160–171

    Article  PubMed  Google Scholar 

  29. Tek H, Gulsun M (2008) Robust vessel tree modeling. In: Proceedings of the 11th international conference on medical image computing and computer assisted interventions, MICCAI’08, vol 5241 of LNCS, pp 602–611

  30. Suryanarayanan S, Mullick R, Mallya Y, Kamath V, Nagaraj N (2004) Automatic partitioning of head CTA for enabling segmentation. In: Fitzpatrick J, Sonka M (eds) SPIE medical imaging, vol 5370, SPIE, San-Diego, pp 410–419. http://dx.doi.org/10.1117/12.533933

  31. Cuisenaire O, Virmani S, Olszewski ME, Ardon R (2008) Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA. Proc SPIE 6914:69143R. http://dx.doi.org/10.1117/12.770481

  32. Cuisenaire O (2009) Fully automated segmentation of carotid and vertebral arteries from CTA. Midas J. http://hdl.handle.net/10380/3100

  33. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient n-d image segmentation. Int J Comput Vision 70: 109–131

    Article  Google Scholar 

  34. Kang L, Xiaodong W, Chen D, Sonka M (2006) Optimal surface segmentation in volumetric images—a graph-theoretic approach. IEEE Trans Pattern Anal Mach Intell 28: 119–134

    Article  Google Scholar 

  35. Kolmogorov V, Boykov Y (2005) What metrics can be approximated by geo-cuts, or global optimization of length/area and flux. In: Proceedings of the tenth IEEE international conference on computer vision, ICCV 2005, vol 1, pp 564–571

  36. Sinop AK, Grady L (2007) A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: Proceedings of the IEEE 11th international conference on computer vision, ICCV 2007, pp 1–8

  37. Vicente S, Kolmogorov V, Rother C (2008) Graph cut based image segmentation with connectivity priors. In: Proceedings of the international conference on computer vision and pattern recognition, CVPR 2008, pp 1–8

  38. Slabaugh G, Unal G (2005) Graph cuts segmentation using an elliptical shape prior. In: Proceedings of the 2005 IEEE international conference on image processing, ICIP’05, vol 2, pp 1222–5

  39. Bauer C, Pock T, Sorantin E, Bischof H, Beichel R (2010) Segmentation of interwoven 3D tubular tree structures utilizing shape priors and graph cuts. Med Image Anal 14: 172–184

    Article  PubMed  Google Scholar 

  40. Schaap M, Neefjes L, Metz C, Giessen A, Weustink A, Mollet N, Wentzel J, Walsum T, Niessen W (2009) Coronary lumen segmentation using graph cuts and robust kernel regression. In: Proceedings of the 21st international conference on information processing in medical imaging, IPMI’09, vol 5636 of LNCS, pp 528–539

  41. Homann H, Vesom G, Noble J (2008) Vasculature segmentation of CT liver images using graph cuts and graph-based analysis. In: IEEE international symposium on biomedical imaging, ISBI’08, pp 53–56

  42. Hameeteman K, Zuluaga MA, Freiman M, Joskowicz L, Cuisenaire O, Flórez Valencia L, Gülsün MA, Krissian K, Mille J, Wong WCK, Orkisz M, Tek H, Hernández Hoyos M, Benmansour F, Chung ACS, Rozie S, van Gils M, van den Borne L, Sosna J, Berman P, Cohen N, Douek PC, Sánchez I, Aissat M, Schaap M, Metz CT, Krestin GP, van der Lugt A, Niessen WJ, van Walsum T (2011) Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading. Med Image Anal 15(4):477–488. doi:10.1016/j.media.2011.02.004. http://www.sciencedirect.com/science/article/pii/S1361841511000260

  43. Freiman M, Frank J, Weizman L, Nammer E, Shilon O, Joskowicz L, Sosna J (2009) Nearly automatic vessels segmentation using graph-based energy minimization. Midas J. http://hdl.handle.net/10380/3090

  44. Roerdink J, Meijster A (2000) The watershed transform: definitions, algorithms and parallelization strategies. Fundam Inf 41: 187–228

    Google Scholar 

  45. Gonzalez RC, Woods RE (2006) Digital image processing, 3rd edn. Prentice-Hall, Inc, Englewood Cliffs, NJ

    Google Scholar 

  46. Freiman M, Eliassaf O, Taieb Y, Joskowicz L, Sosna J (2008) A bayesian approach for liver analysis: algorithm and validation study. In: Proceedings of the 11th international conference on medical image computing and computer aided interventions, MICCAI’08, vol 5241 of LNCS, pp 85–92

  47. Kolmogorov V, Zabih R (2004) What energy functions can be minimized via graph cuts?. IEEE Trans Pattern Anal Mach Intell 26: 147–159

    Article  PubMed  Google Scholar 

  48. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1: 269–271

    Article  Google Scholar 

  49. http://www.itk.org

  50. Freiman M, Broide N, Natanzon M, Weizman L, Nammer E, Shilon O Frank J, Joskowicz L, Sosna J (2009) Vessels-cut: a graph based approach to patient-specific carotid arteries modeling. In: Proceedings of the 2nd 3D physiological human workshop, 3DPH’09, vol 5903 of LNCS, pp 1–12

  51. Antiga L, Steinman D (2004) Robust and objective decomposition and mapping of bifurcating vessels. IEEE Trans Med Imaging 23:704–713. http://www.vmtk.org

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel

    M. Freiman, L. Joskowicz, N. Broide, M. Natanzon & L. Weizman

  2. Simbionix Ltd., Lod, Israel

    E. Nammer & O. Shilon

  3. Department of Radiology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

    J. Sosna

Authors
  1. M. Freiman
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  2. L. Joskowicz
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  3. N. Broide
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  4. M. Natanzon
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  6. O. Shilon
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  7. L. Weizman
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  8. J. Sosna
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Correspondence to M. Freiman.

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Freiman, M., Joskowicz, L., Broide, N. et al. Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation. Int J CARS 7, 799–812 (2012). https://doi.org/10.1007/s11548-012-0673-x

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  • DOI: https://doi.org/10.1007/s11548-012-0673-x

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