Automated segmentation of the esophagus in CT images is of high value to radiologists for oncological examinations of the mediastinum. It can serve as a guideline and prevent confusion with pathological tissue. However, segmentation is a challenging problem due to low contrast and versatile appearance of the esophagus. In this paper, a two step method is proposed which first finds the approximate shape using a "detect and connect" approach. A classifier is trained to find short segments of the esophagus which are approximated by an elliptical model. Recently developed techniques in discriminative learning and pruning of the search space enable a rapid detection of possible esophagus segments. Prior shape knowledge of the complete esophagus is modeled using a Markov chain framework, which allows efficient inferrence of the approximate shape from the detected candidate segments. In a refinement step, the surface of the detected shape is non-rigidly deformed to better fit the organ boundaries. In contrast to previously proposed methods, no user interaction is required. It was evaluated on 117 datasets and achieves a mean segmentation error of 2.28mm with less than 9s computation time. © 2009 Springer-Verlag.
CITATION STYLE
Feulner, J., Zhou, S. K., Cavallaro, A., Seifert, S., Hornegger, J., & Comaniciu, D. (2009). Fast automatic segmentation of the esophagus from 3D CT data using a probabilistic model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5761 LNCS, pp. 255–262). https://doi.org/10.1007/978-3-642-04268-3_32
Mendeley helps you to discover research relevant for your work.