Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model
Stefan Großkopf, Christina Biermann, Kai Ying Deng, Yan Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
We present an efficient algorithm for the robust segmentation of vessel contours in Computed Tomography Angiography (CTA) images. The algorithm performs its task within several steps based on a 3D Active Contour Model (ACM) with refinements on Multi-Planar Reconstructions (MPRs) using 2D ACMs. To be able to distinguish true vessel edges from spurious, an adaptive self-learning edge model is applied. We present details of the algorithm together with an evaluation on n=150 CTA data sets and compare the results of the automatic segmentation with manually outlined contours resulting in a median dice similarity coefficient (DSC) of 92.2%. The algorithm is able to render 100 contours within 1.1s on a Pentium®4 CPU 3.20 GHz, 2 GByte of RAM.