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MICCAI
2009
Springer

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

9 years 5 months ago
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...
Johannes Feulner, Shaohua Kevin Zhou, Alexander Ca
Added 05 Mar 2010
Updated 08 Mar 2010
Type Conference
Year 2009
Where MICCAI
Authors Johannes Feulner, Shaohua Kevin Zhou, Alexander Cavallaro, Sascha Seifert, Joachim Hornegger, Dorin Comaniciu
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