Name
Abstract | ||
|---|---|---|
This paper introduces a probabilistic shortest path approach to extract the esophagus from CT images. In this modality, the absence of strong discriminative features in the observed image make the problem ill-posed without the introduction of additional knowledge constraining the problem. The solution presented in this paper relies on learning and integrating contextual information. The idea is to model spatial dependency between the structure of interest and neighboring organs that may be easier to extract. Observing that the left atrium (LA) and the aorta are such candidates for the esophagus, we propose to learn the esophagus location with respect to these two organs. This dependence is learned from a set of training images where all three structures have been segmented. Each training esophagus is registered to a reference image according to a warping that maps exactly the reference organs. From the registered esophagi, we define the probability of the esophagus centerline relative to the aorta and LA. To extract a new centerline, a probabilistic criterion is defined from a Bayesian formulation that combines the prior information with the image data. Given a new image, the aorta and LA are first segmented and registered to the reference shapes and then, the optimal esophagus centerline is obtained with a shortest path algorithm. Finally, relying on the extracted centerline, coupled ellipse fittings allow a robust detection of the esophagus outer boundary. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1117/12.653657 | Proceedings of SPIE |
Keywords | Field | DocType |
esophagus,segmentation,catheter ablation,shortest path,ellipse fitting | Computer vision,Scale-space segmentation,Shortest path problem,Segmentation,Artificial intelligence,Probabilistic logic,Bayesian formulation,Mathematics,Dijkstra's algorithm | Conference |
Volume | ISSN | Citations |
6144 | 0277-786X | 7 |
PageRank | References | Authors |
0.73 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikael Rousson | 1 | 1002 | 41.09 |
| Ying Bai | 2 | 7 | 0.73 |
| Chenyang Xu | 3 | 585 | 23.07 |
| Frank Sauer | 4 | 173 | 15.07 |