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- W1490755072 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." @default.
- W1490755072 created "2016-06-24" @default.
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- W1490755072 date "2009-01-01" @default.
- W1490755072 modified "2023-10-18" @default.
- W1490755072 title "Fast Automatic Segmentation of the Esophagus from 3D CT Data Using a Probabilistic Model" @default.
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- W1490755072 doi "https://doi.org/10.1007/978-3-642-04268-3_32" @default.
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