Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model

Stefan Grosskopf; Christina Biermann; Kai Deng; Yan Chen

doi:10.1117/12.811364

27 March 2009 Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model

Stefan Grosskopf, Christina Biermann, Kai Deng, Yan Chen

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594D (2009) https://doi.org/10.1117/12.811364
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

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.

Citation Download Citation

Stefan Grosskopf, Christina Biermann, Kai Deng, and Yan Chen "Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594D (27 March 2009); https://doi.org/10.1117/12.811364

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