Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation

Abstract

Purpose

Advanced morphology analysis and image-based hemodynamic simulations are increasingly used to assess the rupture risk of intracranial aneurysms (IAs). However, the accuracy of those results strongly depends on the quality of the vessel wall segmentation.

Methods

To evaluate state-of-the-art segmentation approaches, the Multiple Aneurysms AnaTomy CHallenge (MATCH) was announced. Participants carried out segmentation in three anonymized 3D DSA datasets (left and right anterior, posterior circulation) of a patient harboring five IAs. Qualitative and quantitative inter-group comparisons were carried out with respect to aneurysm volumes and ostia. Further, over- and undersegmentation were evaluated based on highly resolved 2D images. Finally, clinically relevant morphological parameters were calculated.

Results

Based on the contributions of 26 participating groups, the findings reveal that no consensus regarding segmentation software or underlying algorithms exists. Qualitative similarity of the aneurysm representations was obtained. However, inter-group differences occurred regarding the luminal surface quality, number of vessel branches considered, aneurysm volumes (up to 20%) and ostium surface areas (up to 30%). Further, a systematic oversegmentation of the 3D surfaces was observed with a difference of approximately 10% to the highly resolved 2D reference image. Particularly, the neck of the ruptured aneurysm was overrepresented by all groups except for one. Finally, morphology parameters (e.g., undulation and non-sphericity) varied up to 25%.

Conclusions

MATCH provides an overview of segmentation methodologies for IAs and highlights the variability of surface reconstruction. Further, the study emphasizes the need for careful processing of initial segmentation results for a realistic assessment of clinically relevant morphological parameters.

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References

  1. 1.

    Antiga, L., M. Piccinelli, L. Botti, B. Ene-Iordache, A. Remuzzi, and D. A. Steinman. An image-based modeling framework for patient-specific computational hemodynamics. Med. Biol. Eng. Comput. 46:1097–1112, 2008.

    Article  Google Scholar 

  2. 2.

    Berg, P., C. Iosif, S. Ponsonnard, C. Yardin, G. Janiga, and C. Mounayer. Endothelialization of over- and undersized flow-diverter stents at covered vessel side branches: an in vivo and in silico study. J. Biomech. 49:4–12, 2016.

    Article  Google Scholar 

  3. 3.

    Berg, P., C. Roloff, O. Beuing, et al. The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: variability of hemodynamic simulations in two intracranial aneurysms. J. Biomech. Eng. 137:121008, 2015.

    Article  Google Scholar 

  4. 4.

    Berg, P., S. Saalfeld, S. Voß, et al. Does the DSA reconstruction kernel affect hemodynamic predictions in intracranial aneurysms? An analysis of geometry and blood flow variations. J. Neurointerv. Surg. 10:290–296, 2018.

    Article  Google Scholar 

  5. 5.

    Berg, P., D. Stucht, G. Janiga, O. Beuing, O. Speck, and D. Thévenin. Cerebral blood flow in a healthy Circle of Willis and two intracranial aneurysms: computational fluid dynamics versus four-dimensional phase-contrast magnetic resonance imaging. J. Biomech. Eng. 2014. https://doi.org/10.1115/1.4026108.

    Google Scholar 

  6. 6.

    Besl, P. J., and N. D. McKay. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14:239–256, 1992.

    Article  Google Scholar 

  7. 7.

    Bouillot, P., O. Brina, R. Ouared, K. Lovblad, M. Farhat, and V. M. Pereira. Hemodynamic transition driven by stent porosity in sidewall aneurysms. J. Biomech. 48:1300–1309, 2015.

    Article  Google Scholar 

  8. 8.

    Bouillot, P., O. Brina, R. Ouared, K. O. Lovblad, V. M. Pereira, and M. Farhat. Multi-time-lag PIV analysis of steady and pulsatile flows in a sidewall aneurysm. Exp. Fluids 55:145, 2014.

    Article  Google Scholar 

  9. 9.

    Cai, W., C. Hu, J. Gong, and Q. Lan. Anterior communicating artery aneurysm morphology and the risk of rupture. World Neurosurg. 109:119–126, 2018.

    Article  Google Scholar 

  10. 10.

    Cebral, J. R., and H. Meng. Counterpoint: realizing the clinical utility of computational fluid dynamics—closing the gap. Am. J. Neuroradiol. 33:396–398, 2012.

    Article  Google Scholar 

  11. 11.

    Cebral, J. R., F. Mut, M. Raschi, et al. Aneurysm rupture following treatment with flow-diverting stents: computational hemodynamics analysis of treatment. Am. J. Neuroradiol. 32:27–33, 2011.

    Article  Google Scholar 

  12. 12.

    Cebral, J. R., F. Mut, J. Weir, and C. Putman. Quantitative characterization of the hemodynamic environment in ruptured and unruptured brain aneurysms. Am. J. Neuroradiol. 32:145–151, 2011.

    Article  Google Scholar 

  13. 13.

    Cebral, J. R., M. Raschi, F. Mut, et al. Analysis of flow changes in side branches jailed by flow diverters in rabbit models. Int. J. Numer. Method Biomed. Eng. 30:988–999, 2014.

    Article  Google Scholar 

  14. 14.

    Cebral, J. R., M. Vazquez, D. M. Sforza, et al. Analysis of hemodynamics and wall mechanics at sites of cerebral aneurysm rupture. J. Neurointerv. Surg. 7:530–536, 2015.

    Article  Google Scholar 

  15. 15.

    Chen, Y., and G. Medioni. Object modelling by registration of multiple range images. Image Vis. Comput. 10:145–155, 1992.

    Article  Google Scholar 

  16. 16.

    Chnafa, C., O. Brina, V. M. Pereira, and D. A. Steinman. Better than nothing: a rational approach for minimizing the impact of outflow strategy on cerebrovascular simulations. Am. J. Neuroradiol. 39:337–343, 2018.

    Article  Google Scholar 

  17. 17.

    Chnafa, C., K. Valen-Sendstad, O. Brina, V. M. Pereira, and D. A. Steinman. Improved reduced-order modelling of cerebrovascular flow distribution by accounting for arterial bifurcation pressure drops. J. Biomech. 51:83–88, 2017.

    Article  Google Scholar 

  18. 18.

    Chung, B., and J. R. Cebral. CFD for evaluation and treatment planning of aneurysms: review of proposed clinical uses and their challenges. Ann. Biomed. Eng. 43:122–138, 2015.

    Article  Google Scholar 

  19. 19.

    Cito, S., A. J. Geers, M. P. Arroyo, et al. Accuracy and reproducibility of patient-specific hemodynamic models of stented intracranial aneurysms: report on the Virtual Intracranial Stenting Challenge 2011. Ann. Biomed. Eng. 43:154–167, 2015.

    Article  Google Scholar 

  20. 20.

    Dhar, S., M. Tremmel, J. Mocco, et al. Morphology parameters for intracranial aneurysm rupture risk assessment. Neurosurgery 63:185–196; discussion 196–197, 2008.

  21. 21.

    Fiorella, D., C. Sadasivan, H. H. Woo, and B. Lieber. Regarding “Aneurysm rupture following treatment with flow-diverting stents: computational hemodynamics analysis of treatment”. Am. J. Neuroradiol. 32:E95–E97; author reply E98–E100, 2011.

  22. 22.

    Firouzian, A., R. Manniesing, Z. H. Flach, et al. Intracranial aneurysm segmentation in 3D CT angiography: method and quantitative validation with and without prior noise filtering. Eur. J. Radiol. 79:299–304, 2011.

    Article  Google Scholar 

  23. 23.

    Geers, A. J., I. Larrabide, A. G. Radaelli, et al. Reproducibility of image-based computational hemodynamics in intracranial aneurysms: comparison of CTA and 3D-RA. 2009 IEEE Int. Symp. Biomed. Imaging Nano Macro (ISBI), pp. 610–613.

  24. 24.

    Iosif, C., P. Berg, S. Ponsonnard, et al. Role of terminal and anastomotic circulation in the patency of arteries jailed by flow-diverting stents: animal flow model evaluation and preliminary results. J. Neurosurg. 125:898–908, 2016.

    Article  Google Scholar 

  25. 25.

    Iosif, C., P. Berg, S. Ponsonnard, et al. Role of terminal and anastomotic circulation in the patency of arteries jailed by flow-diverting stents: from hemodynamic changes to ostia surface modifications. J. Neurosurg. 126:1702–1713, 2017.

    Article  Google Scholar 

  26. 26.

    Janiga, G., P. Berg, S. Sugiyama, K. Kono, and D. A. Steinman. The Computational Fluid Dynamics Rupture Challenge 2013—Phase I: prediction of rupture status in intracranial aneurysms. Am. J. Neuroradiol. 36:530–536, 2015.

    Article  Google Scholar 

  27. 27.

    Janiga, G., L. Daróczy, P. Berg, D. Thévenin, M. Skalej, and O. Beuing. An automatic CFD-based flow diverter optimization principle for patient-specific intracranial aneurysms. J. Biomech. 48:3846–3852, 2015.

    Article  Google Scholar 

  28. 28.

    Janiga, G., C. Rössl, M. Skalej, and D. Thévenin. Realistic virtual intracranial stenting and computational fluid dynamics for treatment analysis. J. Biomech. 46:7–12, 2013.

    Article  Google Scholar 

  29. 29.

    Kallmes, D. F. Point: CFD—computational fluid dynamics or confounding factor dissemination. Am. J. Neuroradiol. 33:395–396, 2012.

    Article  Google Scholar 

  30. 30.

    Lauric, A., J. E. Hippelheuser, and A. M. Malek. Critical role of angiographic acquisition modality and reconstruction on morphometric and haemodynamic analysis of intracranial aneurysms. J. Neurointerv. Surg. 2018. https://doi.org/10.1136/neurintsurg-2017-013677.

    Google Scholar 

  31. 31.

    Liang, F., X. Liu, R. Yamaguchi, and H. Liu. Sensitivity of flow patterns in aneurysms on the anterior communicating artery to anatomic variations of the cerebral arterial network. J. Biomech. 49:3731–3740, 2016.

    Article  Google Scholar 

  32. 32.

    Ma, D., J. Xiang, H. Choi, et al. Enhanced aneurysmal flow diversion using a dynamic push–pull technique: an experimental and modeling study. Am. J. Neuroradiol. 35:1779–1785, 2014.

    Article  Google Scholar 

  33. 33.

    Meng, H., V. M. Tutino, J. Xiang, and A. Siddiqui. High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis. Am. J. Neuroradiol. 35:1254–1262, 2014.

    Article  Google Scholar 

  34. 34.

    Mihalea, C., J. Caroff, A. Rouchaud, S. Pescariu, J. Moret, and L. Spelle. Treatment of wide-neck bifurcation aneurysm using “WEB Device Waffle Cone Technique”. World Neurosurg. 113:73–77, 2018.

    Article  Google Scholar 

  35. 35.

    Mocco, J., R. D. Brown, J. C. Torner, et al. Aneurysm morphology and prediction of rupture: an international study of unruptured intracranial aneurysms analysis. Neurosurgery 82:491–496, 2018.

    Article  Google Scholar 

  36. 36.

    Murray, C. D. The physiological principle of minimum work: I. The vascular system and the cost of blood volume. Proc. Natl Acad. Sci. USA 12:207–214, 1926.

    Article  Google Scholar 

  37. 37.

    Niemann, U., P. Berg, A. Niemann, et al. Rupture status classification of intracranial aneurysms using morphological parameters. 31st IEEE CBMS Int. Symp. Comput.-Based Med. Syst., Karlstad, Sweden, 2018.

  38. 38.

    Radaelli, A. G., L. Augsburger, J. R. Cebral, et al. Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model—a report on the Virtual Intracranial Stenting Challenge 2007. J. Biomech. 41:2069–2081, 2008.

    Article  Google Scholar 

  39. 39.

    Raghavan, M. L., B. Ma, and R. E. Harbaugh. Quantified aneurysm shape and rupture risk. J. Neurosurg. 102:355–362, 2005.

    Article  Google Scholar 

  40. 40.

    Ren, Y., G. Chen, Z. Liu, Y. Cai, G. Lu, and Z. Li. Reproducibility of image-based computational models of intracranial aneurysm: a comparison between 3D rotational angiography, CT angiography and MR angiography. Biomed. Eng. Online 15:50, 2016.

    Article  Google Scholar 

  41. 41.

    Saalfeld, S., P. Berg, A. Niemann, M. Luz, B. Preim, and O. Beuing. Semi-automatic neck curve reconstruction for intracranial aneurysm rupture risk assessment based on morphological parameters. Int. J. Comput. Assist. Radiol. Surg. 2018 (accepted for publication).

  42. 42.

    Skodvin, T. Ø., Ø. Evju, A. Sorteberg, and J. G. Isaksen. Prerupture intracranial aneurysm morphology in predicting risk of rupture: a matched case–control study. Neurosurgery 2018. https://doi.org/10.1093/neuros/nyy010.

    Google Scholar 

  43. 43.

    Steinman, D. A. Computational modeling and flow diverters: a teaching moment. Am. J. Neuroradiol. 32:981–983, 2011.

    Article  Google Scholar 

  44. 44.

    Steinman, D. A., Y. Hoi, P. Fahy, et al. Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge. J. Biomech. Eng. 135:21016, 2013.

    Article  Google Scholar 

  45. 45.

    Valen-Sendstad, K., A. W. Bergersen, Y. Shimogonya, et al. Real-world variability in the prediction of intracranial aneurysm wall shear stress: the 2015 International Aneurysm CFD Challenge. Cardiovasc. Eng. Technol. 2018 (accepted for publication).

  46. 46.

    Valen-Sendstad, K., and D. A. Steinman. Mind the gap: impact of computational fluid dynamics solution strategy on prediction of intracranial aneurysm hemodynamics and rupture status indicators. Am. J. Neuroradiol. 35:536–543, 2014.

    Article  Google Scholar 

  47. 47.

    Wang, Y., Y. Zhang, L. Navarro, et al. Multilevel segmentation of intracranial aneurysms in CT angiography images. Med. Phys. 43:1777, 2016.

    Article  Google Scholar 

  48. 48.

    Xiang, J., S. K. Natarajan, M. Tremmel, et al. Hemodynamic–morphologic discriminants for intracranial aneurysm rupture. Stroke 42:144–152, 2011.

    Article  Google Scholar 

  49. 49.

    Xiang, J., V. M. Tutino, K. V. Snyder, and H. Meng. CFD: computational fluid dynamics or confounding factor dissemination? The role of hemodynamics in intracranial aneurysm rupture risk assessment. Am. J. Neuroradiol. 35:1849–1857, 2014.

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge Thomas Hoffmann and Dr. Axel Boese (University of Magdeburg, Germany) for their assistance regarding the challenge design.

Funding

This study was funded by the Federal Ministry of Education and Research in Germany within the Forschungscampus STIMULATE (Grant Number 13GW0095A) and the German Research Foundation (Grant Number 399581926).

Conflict of interest

Authors Philipp Berg, Samuel Voß, Sylvia Saalfeld, Gábor Janiga, Aslak W. Bergersen, Kristian Valen-Sendstad, Jan Bruening, Leonid Goubergrits, Andreas Spuler, Nicole M. Cancelliere, David A. Steinman, Vitor M. Pereira, Tin Lok Chiu, Anderson Chun On Tsang, Bong Jae Chung, Juan R. Cebral, Salvatore Cito, Jordi Pallarès, Gabriele Copelli, Benjamin Csippa, György Paál, Soichiro Fujimura, Hiroyuki Takao, Simona Hodis, Georg Hille, Christof Karmonik, Saba Elias, Kerstin Kellermann, Muhammad Owais Khan, Alison L. Marsden, Hernán G. Morales, Senol Piskin, Ender A. Finol, Mariya Pravdivtseva, Hamidreza Rajabzadeh-Oghaz, Nikhil Paliwal, Hui Meng, Santhosh Seshadhri, Matthew Howard, Masaaki Shojima, MD, Shin-ichiro Sugiyama, Kuniyasu Niizuma, Sergey Sindeev, Sergey Frolov, Thomas Wagner, Alexander Brawanski, Yi Qian, Yu-An Wu, Kent Carlson, Dan Dragomir-Daescu, and Oliver Beuing declare that they have no conflicts of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors. Institutional Review Board approval was obtained from University Hospital Magdeburg for sharing of the anonymized images.

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Correspondence to Philipp Berg.

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Associate Editors Francesco Migliavacca and Ajit P. Yoganathan oversaw the review of this article.

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Fig. A1

Supplementary material 1 (PNG 2485 kb) Segmentation results of each group for aneurysms A and B (right MCA) from one perspective. Notice the inconsistencies with respect to surface smoothness, aneurysm neck representation and number of side branches considered.

Fig. A2

Supplementary material 2 (PNG 2760 kb) Segmentation results of each group for aneurysm C and D (left MCA) from one perspective. Notice the inconsistencies with respect to surface smoothness, aneurysm neck representation and number of side branches considered.

Table S3

Supplementary material 3 (XLSX 40 kb) Cross-sectional areas of the in- and outflow vessels (mm2) for all three datasets. Empty fields indicate the absence of the corresponding vessel.

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Berg, P., Voß, S., Saalfeld, S. et al. Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation. Cardiovasc Eng Tech 9, 565–581 (2018). https://doi.org/10.1007/s13239-018-00376-0

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Keywords

  • Challenge
  • Intracranial aneurysm
  • Morphology
  • Segmentation