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.
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.
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%.
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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Chen, Y., and G. Medioni. Object modelling by registration of multiple range images. Image Vis. Comput. 10:145–155, 1992.
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.
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.
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.
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.
Dhar, S., M. Tremmel, J. Mocco, et al. Morphology parameters for intracranial aneurysm rupture risk assessment. Neurosurgery 63:185–196; discussion 196–197, 2008.
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.
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.
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.
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.
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.
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.
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.
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.
Kallmes, D. F. Point: CFD—computational fluid dynamics or confounding factor dissemination. Am. J. Neuroradiol. 33:395–396, 2012.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Raghavan, M. L., B. Ma, and R. E. Harbaugh. Quantified aneurysm shape and rupture risk. J. Neurosurg. 102:355–362, 2005.
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.
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).
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.
Steinman, D. A. Computational modeling and flow diverters: a teaching moment. Am. J. Neuroradiol. 32:981–983, 2011.
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.
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).
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.
Wang, Y., Y. Zhang, L. Navarro, et al. Multilevel segmentation of intracranial aneurysms in CT angiography images. Med. Phys. 43:1777, 2016.
Xiang, J., S. K. Natarajan, M. Tremmel, et al. Hemodynamic–morphologic discriminants for intracranial aneurysm rupture. Stroke 42:144–152, 2011.
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.
The authors acknowledge Thomas Hoffmann and Dr. Axel Boese (University of Magdeburg, Germany) for their assistance regarding the challenge design.
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.
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.
Associate Editors Francesco Migliavacca and Ajit P. Yoganathan oversaw the review of this article.
Electronic supplementary material
Below is the link to the electronic supplementary material.
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.
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.
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.
About this article
Cite this article
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
- Intracranial aneurysm