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Proceedings Paper

Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model
Author(s): Stefan Grosskopf; Christina Biermann; Kai Deng; Yan Chen
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Paper 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.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594D (27 March 2009); doi: 10.1117/12.811364
Show Author Affiliations
Stefan Grosskopf, Siemens Healthcare Sector (Germany)
Christina Biermann, Siemens Healthcare Sector (Germany)
Eberhard-Karls-Univ. of Tuebingen (Germany)
Kai Deng, Wuhan Union Hospital, HUST Tongji Medical School (China)
Yan Chen, Shandong Medical Imaging Research Institute (China)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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