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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Computer-assisted 3D morphology analysis can improve accuracy and consistency in measurements compared with manual 2D measurements. It can also more reliably quantify shape irregularity using the UI. Future application of computer-assisted analysis tools could help clinicians standardize morphology evaluations, leading to more consistent IA evaluations.

Background and Purpose Several recent prospective studies have found that unruptured intracranial aneurysms (IAs) at various anatomical locations have different propensities for future rupture. However, there remains a lack of understanding regarding the rupture-prone characteristics, such as morphologic and hemodynamic factors, associated with different IA locations. Materials and Methods We investigated the characteristics of 311 unruptured aneurysms at our center. Based on the PHASES study, we separated and compared morphologic and hemodynamic characteristics among three aneurysm location groups: 1. internal carotid artery (ICA), 2. middle cerebral artery (MCA), and 3. anterior communicating (ACOM), posterior communicating (PCOM) and posterior circulation arteries. Results A mixed model statistical analysis showed that size ratio, low wall shear stress area and pressure loss coefficient were different between the IA location groups. Additionally, a pairwise comparison showed that ICA aneurysms had lower size ratios, lower wall shear stress areas and lower pressure loss coefficients compared to MCA aneurysms, and compared to the group of ACOM, PCOM and posterior circulation aneurysms. There was no statistical differences between MCA aneurysms and the group of ACOM, PCOM and posterior circulation aneurysms for morphologic or hemodynamic characteristics. Conclusions ICA aneurysms may be subjected to less rupture-prone morphologic and hemodynamic characteristics compared to other locations, which could explain the decreased rupture propensity of IAs at this location.

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Neurosurgeons currently base most of their treatment decisions for intracranial aneurysms (IAs) on morphological measurements made manually from 2D angiographic images. These measurements tend to be inaccurate because 2D measurements cannot capture the complex geometry of IAs and because manual measurements are variable depending on the clinician's experience and opinion. Incorrect morphological measurements may lead to inappropriate treatment strategies. In order to improve the accuracy and consistency of morphological analysis of IAs, we have developed an image-based computational tool, AView. In this study, we quantified the accuracy of computer-assisted adjuncts of AView for aneurysmal morphologic assessment by performing measurement on spheres of known size and anatomical IA models. AView has an average morphological error of 0.56% in size and 2.1% in volume measurement. We also investigate the clinical utility of this tool on a retrospective clinical dataset and compare size and neck diameter measurement between 2D manual and 3D computer-assisted measurement. The average error was 22% and 30% in the manual measurement of size and aneurysm neck diameter, respectively. Inaccuracies due to manual measurements could therefore lead to wrong treatment decisions in 44% and inappropriate treatment strategies in 33% of the IAs. Furthermore, computer-assisted analysis of IAs improves the consistency in measurement among clinicians by 62% in size and 82% in neck diameter measurement. We conclude that AView dramatically improves accuracy for morphological analysis. These results illustrate the necessity of a computer-assisted approach for the morphological analysis of IAs.