Automated Cutting Plane Positioning for Intracranial Aneurysm Quantification

Objective: Aneurysm rupture risk can be assessed by its morphologic and hemodynamics features extracted based on angiographic images. Feature extraction entails aneurysm isolation, typically by manually positioning a cutting plane (MCP). To eliminate intra- and inter-rater variabilities, we propose automatic cutting plane (ACP) positioning based on the analysis of vascular surface mesh. Methods: Innovative Hough-like and multi-hypothesis-based detection of aneurysm center, parent vessel inlets, and centerlines were proposed. These were used for initialization and iterative ACP positioning by geometry-inspired cost function optimization. For validation and baseline comparison, we tested MCP and manual neck curve-based isolation. Isolated aneurysm morphology was characterized by size, dome height, aspect ratio, and nonsphericity index. Results: Methods were applied to 55 intracranial saccular aneurysms from two sites, involving 3-D digital subtraction angiography, computed tomography angiography, and magnetic resonance angiography modalities. Isolation based on ACP resulted in smaller average inter-curve distances (AICDs), compared to those obtained by MCP. One case had AICD higher than 1.0 mm, while 90% of cases had AICD $< $ 0.5 mm. Intra- and inter-rater AICD variability of manual neck curves was higher compared to MCP, validating its robustness for clinical purposes. Conclusion: The ACP method achieved high accuracy and reliability of aneurysm isolation, also confirmed by expert visual analysis. So extracted morphologic features were in good agreement with MCP-based ones, therefore, ACP has great potential for aneurysm morphology and hemodynamics quantification in clinical applications. Significance: The novel method is angiographic modality agnostic; it delivers repeatable isolation important in follow-up aneurysm assessment; its performance is comparable to MCP; and re-evaluation is fast and simple.