Fast Automatic Segmentation of the Esophagus from 3D CT Data Using a Probabilistic Model

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 or...

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Johannes Feulner, Shaohua Kevin Zhou, Alexander Cavallaro, Sascha Seifert, Joachim Hornegger, Dorin Comaniciu
• Keywords: Approximate Shape, Complete Esophagus, Medical Imaging, MICCAI 2009, Possible Esophagus Segments