Probabilistic minimal path for automated esophagus segmentation

Mikael Rousson; Ying Bai; Chenyang Xu; Frank Sauer

doi:10.1117/12.653657

15 March 2006 Probabilistic minimal path for automated esophagus segmentation

Mikael Rousson, Ying Bai, Chenyang Xu, Frank Sauer

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 614449 (2006) https://doi.org/10.1117/12.653657
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

This paper introduces a probabilistic shortest path approach to extract the esophagus from CT images. In this modality, the absence of strong discriminative features in the observed image make the problem ill-posed without the introduction of additional knowledge constraining the problem. The solution presented in this paper relies on learning and integrating contextual information. The idea is to model spatial dependency between the structure of interest and neighboring organs that may be easier to extract. Observing that the left atrium (LA) and the aorta are such candidates for the esophagus, we propose to learn the esophagus location with respect to these two organs. This dependence is learned from a set of training images where all three structures have been segmented. Each training esophagus is registered to a reference image according to a warping that maps exactly the reference organs. From the registered esophagi, we define the probability of the esophagus centerline relative to the aorta and LA. To extract a new centerline, a probabilistic criterion is defined from a Bayesian formulation that combines the prior information with the image data. Given a new image, the aorta and LA are first segmented and registered to the reference shapes and then, the optimal esophagus centerline is obtained with a shortest path algorithm. Finally, relying on the extracted centerline, coupled ellipse fittings allow a robust detection of the esophagus outer boundary.

Citation Download Citation

Mikael Rousson, Ying Bai, Chenyang Xu, and Frank Sauer "Probabilistic minimal path for automated esophagus segmentation", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614449 (15 March 2006); https://doi.org/10.1117/12.653657

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