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

文献类型： 会议论文

第一作者： Shijun Wang

作者： Shijun Wang 1 ; Brandon Peplinski 1 ; Le Lu 1 ; Weidong Zhang 1 ; Jianfei Liu 1 ; Zhuoshi Wei 1 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,Radiology and Imaging Sciences, National Institutes of Health Clinical Center,Bethesda, MD, 20892-1182, U.S.

关键词： Sequential Monte Carlo tracking;multiple cues;particle filtering;marginal artery;CT angiography

会议名称： International conference on medical imaging computing and computer-assisted intervention

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页码： 518-525

摘要： 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.

分类号： r312

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