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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013

Volume 8150 of the series Lecture Notes in Computer Science pp 518-525

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

  • Shijun WangAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center
  • , Brandon PeplinskiAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center
  • , Le LuAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center
  • , Weidong ZhangAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center
  • , Jianfei LiuAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center
  • , Zhuoshi WeiAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center
  • , Ronald M. SummersAffiliated withCarnegie Mellon UniversityImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center

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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