Philipp Berg1,2, Samuel Voß3,4, Sylvia Saalfeld5,4, Gábor Janiga3,4, Aslak W Bergersen6, Kristian Valen-Sendstad6, Jan Bruening7, Leonid Goubergrits7, Andreas Spuler8, Nicole M Cancelliere9, David A Steinman10, Vitor M Pereira9,11, Tin Lok Chiu12, Anderson Chun On Tsang13, Bong Jae Chung14, Juan R Cebral14, Salvatore Cito15, Jordi Pallarès15, Gabriele Copelli16, Benjamin Csippa17, György Paál17, Soichiro Fujimura18,19, Hiroyuki Takao18,19,20, Simona Hodis21, Georg Hille5, Christof Karmonik22, Saba Elias22, Kerstin Kellermann23, Muhammad Owais Khan24, Alison L Marsden24, Hernán G Morales25,26, Senol Piskin27,28, Ender A Finol27, Mariya Pravdivtseva29, Hamidreza Rajabzadeh-Oghaz30,31, Nikhil Paliwal30,31, Hui Meng30,31, Santhosh Seshadhri32, Matthew Howard33, Masaaki Shojima34, Shin-Ichiro Sugiyama35, Kuniyasu Niizuma35, Sergey Sindeev36, Sergey Frolov36, Thomas Wagner37,38, Alexander Brawanski37, Yi Qian39, Yu-An Wu40, Kent D Carlson40, Dan Dragomir-Daescu40, Oliver Beuing41,4. 1. Department of Fluid Dynamics and Technical Flows, University of Magdeburg, Magdeburg, Germany. berg@ovgu.de. 2. Forschungscampus STIMULATE, Magdeburg, Germany. berg@ovgu.de. 3. Department of Fluid Dynamics and Technical Flows, University of Magdeburg, Magdeburg, Germany. 4. Forschungscampus STIMULATE, Magdeburg, Germany. 5. Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany. 6. Department of Computational Physiology, Simula Research Laboratory, Lysaker, Norway. 7. Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany. 8. Neurosurgery Department, Helios Hospital Berlin Buch, Berlin, Germany. 9. Division of Neuroradiology, Joint Department of Medical Imaging, Toronto Western Hospital, University Health Network, Toronto, ON, Canada. 10. Biomedical Simulation Laboratory, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada. 11. Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada. 12. Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong. 13. Division of Neurosurgery, Department of Surgery, The University of Hong Kong, Pokfulam, Hong Kong. 14. Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, VA, USA. 15. Departament d'Enginyeria Mecànica, Universitat Rovira i Virgili, Tarragona, Spain. 16. Department of Industrial Engineering, University of Parma, Parma, Italy. 17. Department of Hydrodynamic Systems, Budapest University of Technology and Economics, Budapest, Hungary. 18. Graduate School of Mechanical Engineering, Tokyo University of Science, Katsushika-ku, Tokyo, Japan. 19. Department of Innovation for Medical Information Technology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan. 20. Department of Neurosurgery, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan. 21. Department of Mathematics, Texas A&M University, Kingsville, USA. 22. MRI Core, Houston Methodist Research Institute, Houston, TX, USA. 23. Dornheim Medical Images GmbH, Magdeburg, Germany. 24. Stanford University, Stanford, CA, USA. 25. Philips Research, Paris, France. 26. Centro de Investigación en Fisiología del Ejercicio, Facultad de Ciencias, Universidad Mayor, Santiago de Chile, Chile. 27. Department of Mechanical Engineering, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA. 28. Department of Mechanical Engineering, Koc University, Rumelifeneri Kampusu, 34450, Istanbul, Turkey. 29. Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein UKSH, Kiel, Germany. 30. Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA. 31. Canon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA. 32. Medtronic Engineering Innovation Centre, Hyderabad, India. 33. Synopsys Inc., San Diego, CA, USA. 34. Saitama Medical University General Hospital, 1981 Kamoda, Kawagoe, Saitama, 350-8655, Japan. 35. Department of Neurosurgery, Tohoku University Graduate of Medicine, Sendai, Japan. 36. Department of Biomedical Engineering, Tambov State Technical University, Tambov, Russia. 37. University Hospital Regensburg, Regensburg, Germany. 38. University of Applied Sciences Regensburg, Regensburg, Germany. 39. Macquarie University, Sydney, Australia. 40. Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA. 41. Institute of Neuroradiology, University Hospital Magdeburg, Magdeburg, Germany.
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.
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.
Authors: Sylvia Saalfeld; Samuel Voß; Oliver Beuing; Bernhard Preim; Philipp Berg Journal: Int J Comput Assist Radiol Surg Date: 2019-07-30 Impact factor: 2.924
Authors: Philipp Berg; Samuel Voß; Gábor Janiga; Sylvia Saalfeld; Aslak W Bergersen; Kristian Valen-Sendstad; Jan Bruening; Leonid Goubergrits; Andreas Spuler; Tin Lok Chiu; Anderson Chun On Tsang; Gabriele Copelli; Benjamin Csippa; György Paál; Gábor Závodszky; Felicitas J Detmer; Bong J Chung; Juan R Cebral; Soichiro Fujimura; Hiroyuki Takao; Christof Karmonik; Saba Elias; Nicole M Cancelliere; Mehdi Najafi; David A Steinman; Vitor M Pereira; Senol Piskin; Ender A Finol; Mariya Pravdivtseva; Prasanth Velvaluri; Hamidreza Rajabzadeh-Oghaz; Nikhil Paliwal; Hui Meng; Santhosh Seshadhri; Sreenivas Venguru; Masaaki Shojima; Sergey Sindeev; Sergey Frolov; Yi Qian; Yu-An Wu; Kent D Carlson; David F Kallmes; Dan Dragomir-Daescu; Oliver Beuing Journal: Int J Comput Assist Radiol Surg Date: 2019-05-03 Impact factor: 2.924
Authors: Kimberly A Stevens Boster; Tanmay C Shidhore; Aaron A Cohen-Gadol; Ivan C Christov; Vitaliy L Rayz Journal: Cardiovasc Eng Technol Date: 2022-02-01 Impact factor: 2.495
Authors: Sarah N Lipp; Elizabeth E Niedert; Hannah L Cebull; Tyler C Diorio; Jessica L Ma; Sean M Rothenberger; Kimberly A Stevens Boster; Craig J Goergen Journal: Front Physiol Date: 2020-05-12 Impact factor: 4.566
Authors: Naomi Larsen; Charlotte Flüh; Sylvia Saalfeld; Samuel Voß; Georg Hille; David Trick; Fritz Wodarg; Michael Synowitz; Olav Jansen; Philipp Berg Journal: Neuroradiology Date: 2020-07-17 Impact factor: 2.804