Title
Sequential Monte Carlo tracking for marginal artery segmentation on CT angiography by multiple cue fusion.
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.
Year
DOI
Venue
2013
10.1007/978-3-642-40763-5_64
Lecture Notes in Computer Science
Keywords
Field
DocType
Sequential Monte Carlo tracking,multiple cues,particle filtering,marginal artery,CT angiography
Computer vision,Monte Carlo method,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Particle filter,Posterior probability,Robustness (computer science),Artificial intelligence,Marginal artery of the colon,Bayesian probability
Conference
Volume
Issue
ISSN
8150
Pt 2
0302-9743
Citations 
PageRank 
References 
4
0.40
8
Authors
7
Name
Order
Citations
PageRank
Shijun Wang123922.83
Brandon Peplinski250.75
Le Lu3129786.78
Weidong Zhang4192.52
Jianfei Liu58112.98
Zhuoshi Wei614310.99
Ronald M Summers7131.17