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.