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.

DOI: 10.1007/978-3-642-04268-3_32

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@article{Feulner2009FastAS, title={Fast Automatic Segmentation of the Esophagus from 3D CT Data Using a Probabilistic Model}, author={Johannes Feulner and Shaohua Kevin Zhou and Alexander Cavallaro and Sascha Seifert and Joachim Hornegger and Dorin Comaniciu}, journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2009}, volume={12 Pt 1}, pages={255-62} }