Fast Automatic Segmentation of the Esophagus from 3D CT data using a Probabilistic Model

Feulner J, Zhou SK, Seifert S, Hornegger J, Comaniciu D, Cavallaro AJ (2009)

Publication Type: Authored book, Volume of book series

Publication year: 2009

Journal

Lecture Notes in Computer Science Springer Verlag

Original Authors: Feulner J., Zhou S., Cavallaro A., Seifert S., Hornegger J., Comaniciu D.

Publisher: Springer-verlag

City/Town: Berlin

Book Volume: null

Pages Range: 255-262

Event location: London

Journal Issue: null

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

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. © 2009 Springer-Verlag.

Authors with CRIS profile

Johannes Feulner Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Joachim Hornegger Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Alexander Josef Cavallaro Institute of Radiology

Involved external institutions

Siemens Corporate Research US United States (USA) (US) Siemens AG, Sector Corporate Technology DE Germany (DE)

How to cite

APA:

Feulner, J., Zhou, S.K., Seifert, S., Hornegger, J., Comaniciu, D., & Cavallaro, A.J. (2009). Fast Automatic Segmentation of the Esophagus from 3D CT data using a Probabilistic Model. Berlin: Springer-verlag.

MLA:

Feulner, Johannes, et al. Fast Automatic Segmentation of the Esophagus from 3D CT data using a Probabilistic Model. Berlin: Springer-verlag, 2009.

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