title: Fast automatic segmentation of the esophagus from 3D CT data using a probabilistic model.
abstract: 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.
authors.affiliation: Chair of Pattern Recognition, University of Erlangen-Nuremberg, Germany. johannes.feulner@informatik.uni-erlangen.de
authors.forename: Johannes
authors.initials: J
authors.lastname: Feulner
authors.forename: S Kevin
authors.initials: SK
authors.lastname: Zhou
authors.forename: Alexander
authors.initials: A
authors.lastname: Cavallaro
authors.forename: Sascha
authors.initials: S
authors.lastname: Seifert
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003199:Computer Systems; D004947:Esophagus / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*