Investigation Of Image Segmentation Methods
For Intracranial Aneurysm Haemodynamic
Research
Author(s)
Y. Sen, Y. Zhang, Y. Qian & M. Morgan
Abstract
Patient-specific haemodynamic technology has been applied in clinical
applications. Computational haemodynamic simulation is performed by
utilization of geometric results obtained via medical image segmentation.
However, the geometry and volume of intracranial aneurysm models are highly
dependent upon different segmentation methods, even when employed upon the
same medical imaging data. Moreover, methods of vascular segmentation have
been insufficiently validated. In this study, we compared three segmentation
methods; the Region Growing Threshold (RGT), Chan-Vese model (CV) and
Threshold-Based Level Set (TLS), to segment the aneurysm geometry through
the use of CTA image data. The results were evaluated via measurement of
arterial volume differences (VD), local geometric shapes, and haemodynamic
simulation results. We found that the maximum VD of three segmentation
methods sat at around ±15%. Local artery anatomical shapes of aneurysms were
likewise found to significantly influence segmentation results. The
computational haemodynamic simulation was performed modelling three types
of geometries, with typical haemodynamic characteristics; i.e. energy loss and
shear stress. We found that there was a maximum of 58% difference between
segmentation methods. The results indicated that it is essential to validate
segmentation methods in order to confirm the quality of segmentation processes
in the application of patient-specific cerebrovascular haemodynamic analysis.
Keywords: medical image segmentation, intracranial aneurysm, level set, region
growing threshold, haemodynamic.
Keywords
Keywords: medical image segmentation, intracranial aneurysm, level set, region
growing threshold, haemodynamic.
Related Book
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