Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression
We formulate vessel detection on contrast-enhanced computed tomography angiogram images as a Bayesian tracking problem. A new vessel detection method by fusing multiple cues extracted from CT images. Use of colon atlas and random forest to prune tracking results and reduce false positives. Our proposed method showed a significant increase in the F1 score when compared to the traditional Hessian vesselness method.
sequential monte carlo tracking marginal artery multiple cue fusion random forest ct angiography