Level set methods have become increasingly popular as a framework for image segmentation. Yet when used as
a generic segmentation tool, they suffer from an important drawback: Current formulations do not allow much
user interaction. Upon initialization, boundaries propagate to the final segmentation without the user being able
to guide or correct the segmentation. In the present work, we address this limitation by proposing a probabilistic
framework for image segmentation which integrates input intensity information and user interaction on equal
footings. The resulting algorithm determines the most likely segmentation given the input image and the user
input. In order to allow a user interaction in real-time during the segmentation, the algorithm is implemented
on a graphics card and in a narrow band formulation.
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.