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

Authors:
Johannes Feulner;S. Kevin Zhou;Alexander Cavallaro;Sascha Seifert;Joachim Hornegger;Dorin Comaniciu
Affiliations:
Chair of Pattern Recognition, University of Erlangen-Nuremberg, Germany and Siemens Corporate Technology, Erlangen, Germany;Siemens Corporate Research, Princeton, USA;Radiology Institute, University Hospital Erlangen, Germany;Siemens Corporate Technology, Erlangen, Germany;Chair of Pattern Recognition, University of Erlangen-Nuremberg, Germany and Erlangen Graduate School in Advanced Optical Technologies (SAOT), Germany;Siemens Corporate Research, Princeton, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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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.