Pre-clinical evaluation of Implicit Deformable Models for three-dimensional segmentation of brain aneurysms in CTA


Journal Article

Knowledge of brain aneurysm dimensions is essential during the planning stage of minimally invasive surgical interventions using Guglielmi Detachable Coils (GDC). These parameters are obtained in clinical routine using 2D Maximum Intensity Projection images from Computed Tomographic Angiography (CTA). Automated quantification of the three dimensional structure of aneurysms directly from the 3D data set may be used to provide accurate and objective measurements of the clinically relevant parameters. The properties of Implicit Deformable Models make them suitable to accurately extract the three dimensional structure of the aneurysm and its connected vessels. We have devised a two-stage segmentation algorithm for this purpose. In the first stage, a rough segmentation is obtained by means of the Fast Marching Method combining a speed function based on a vessel enhancement filtering and a freezing algorithm. In the second stage, this rough segmentation provides the initialization for Geodesic Active Contours driven by region-based information. The latter problem is solved using the Level Set algorithm. This work presents a comparative study between a clinical and a computerized protocol to derive three geometrical descriptors of aneurysm morphology that are standard in assessing the viability of surgical treatment with GDCs. The study was performed on a data base of 40 brain aneurysms. The manual measurements were made by two neuroradiologists in two independent sessions. Both inter- and intra-observer variability and comparison with the automated method are presented. According to these results, Implicit Deformable Models are a suitable technique for this application.

Full Text

Duke Authors

Cited Authors

  • Hernandez, M; Barrena, R; Hernandez, G; Sapiro, G; Frangi, AF

Published Date

  • September 15, 2003

Published In

Volume / Issue

  • 5032 II /

Start / End Page

  • 1264 - 1274

International Standard Serial Number (ISSN)

  • 0277-786X

Digital Object Identifier (DOI)

  • 10.1117/12.483596

Citation Source

  • Scopus