Three-dimensional morphological analysis of intracranial aneurysms: a fully automated method for aneurysm sac isolation and quantification

Med Phys. 2011 May;38(5):2439-49. doi: 10.1118/1.3575417.


Purpose: Morphological descriptors are practical and essential biomarkers for diagnosis and treatment selection for intracranial aneurysm management according to the current guidelines in use. Nevertheless, relatively little work has been dedicated to improve the three-dimensional quantification of aneurysmal morphology, to automate the analysis, and hence to reduce the inherent intra and interobserver variability of manual analysis. In this paper we propose a methodology for the automated isolation and morphological quantification of saccular intracranial aneurysms based on a 3D representation of the vascular anatomy.

Method: This methodology is based on the analysis of the vasculature skeleton's topology and the subsequent application of concepts from deformable cylinders. These are expanded inside the parent vessel to identify different regions and discriminate the aneurysm sac from the parent vessel wall. The method renders as output the surface representation of the isolated aneurysm sac, which can then be quantified automatically. The proposed method provides the means for identifying the aneurysm neck in a deterministic way. The results obtained by the method were assessed in two ways: they were compared to manual measurements obtained by three independent clinicians as normally done during diagnosis and to automated measurements from manually isolated aneurysms by three independent operators, nonclinicians, experts in vascular image analysis. All the measurements were obtained using in-house tools. The results were qualitatively and quantitatively compared for a set of the saccular intracranial aneurysms (n = 26).

Results: Measurements performed on a synthetic phantom showed that the automated measurements obtained from manually isolated aneurysms where the most accurate. The differences between the measurements obtained by the clinicians and the manually isolated sacs were statistically significant (neck width: p <0.001, sac height: p = 0.002). When comparing clinicians' measurements to automatically isolated sacs, only the differences for the neck width were significant (neck width: p <0.001, sac height: p = 0.95). However, the correlation and agreement between the measurements obtained from manually and automatically isolated aneurysms for the neck width: p = 0.43 and sac height: p = 0.95 where found.

Conclusions: The proposed method allows the automated isolation of intracranial aneurysms, eliminating the interobserver variability. In average, the computational cost of the automated method (2 min 36 s) was similar to the time required by a manual operator (measurement by clinicians: 2 min 51 s, manual isolation: 2 min 21 s) but eliminating human interaction. The automated measurements are irrespective of the viewing angle, eliminating any bias or difference between the observer criteria. Finally, the qualitative assessment of the results showed acceptable agreement between manually and automatically isolated aneurysms.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence
  • Cerebral Angiography / methods*
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Intracranial Aneurysm / diagnostic imaging*
  • Pattern Recognition, Automated / methods*
  • Radiographic Image Enhancement / methods
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity