Image segmentation methods for intracranial aneurysm haemodynamic research

Abstract

Patient-specific haemodynamic technology is being increasingly utilised in clinical applications. Under normal circumstances, computational haemodynamic simulation is performed using geometric results obtained via medical image segmentation. However, even when employed upon the same set of medical imaging data, both the geometry and volume of intracranial aneurysm models are highly dependent upon varying insufficiently validated vascular segmentation methods. In this study, we compared three segmentation methods to segment the geometry of the aneurysm. These include: the Region Growing Threshold (RGT), Chan-Vese model (CV) and Threshold-Based Level Set (TLS). The results obtained were evaluated via measurement of arterial volume differences (VD), local geometric shapes, and haemodynamic simulation results. In total, 45 patient-specific aneurysm cases with three different anatomy locations were assessed in this study. From this, we discovered that the average VD of all three segmentation methods lay in the vicinity of 9.3% (SD=±4.6%). The computational haemodynamic simulation was performed via the use of the vessel geometries. Analyses produced an average of 23.2% (SD=±8.7%) difference in energy loss (EL) between the varying segmentation methods, with the difference in Wall Shear Stress (WSS) averaging 24.0% (SD=±8.5%) and 126.4% (SD=±124.4%) for the highest and lowest volumes of WSS respectively. The results of the lowest WSS, was seen to be significantly dependent upon the geometry of the aneurysm surface. It is therefore essential, in order to confirm the quality of segmentation processes in the application of patient-specific analyses of cerebrovascular haemodynamics – to validate these individual segmentation methods.