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Dr.-Ing. Johannes Feulner

Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Providing semantic access to medical image databases

Fast Automatic Segmentation of the Esophagus

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. We developed a two
step method 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.

Example of a segmentation result. The yellow boxes show the result of the Markov chain based path inference step. The final segmentation is shown as blue contour.