Sequential Monte Carlo Tracking for Marginal Artery Segmentation on CT Angiography by Multiple Cue Fusion

  • Shijun Wang
  • Brandon Peplinski
  • Le Lu
  • Weidong Zhang
  • Jianfei Liu
  • Zhuoshi Wei
  • Ronald M. Summers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

In this work we formulate vessel segmentation on contrast-enhanced CT angiogram images as a Bayesian tracking problem. To obtain posterior probability estimation of vessel location, we employ sequential Monte Carlo tracking and propose a new vessel segmentation method by fusing multiple cues extracted from CT images. These cues include intensity, vesselness, organ detection, and bridge information for poorly enhanced segments from global path minimization. By fusing local and global information for vessel tracking, we achieved high accuracy and robustness, with significantly improved precision compared to a traditional segmentation method (p=0.0002). Our method was applied to the segmentation of the marginal artery of the colon, a small bore vessel of potential importance for colon segmentation and CT colonography. Experimental results indicate the effectiveness of the proposed method.

Keywords

Sequential Monte Carlo tracking multiple cues particle filtering marginal artery CT angiography 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shijun Wang
    • 1
  • Brandon Peplinski
    • 1
  • Le Lu
    • 1
  • Weidong Zhang
    • 1
  • Jianfei Liu
    • 1
  • Zhuoshi Wei
    • 1
  • Ronald M. Summers
    • 1
  1. 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging SciencesNational Institutes of Health Clinical CenterBethesdaU.S.

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