Conference Paper

Automatic Detection of Air Holes Inside the Esophagus in CT Images.

DOI: 10.1007/978-3-540-78640-5_80 Conference: Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin
Source: DBLP

ABSTRACT Air holes inside the esophagus can be used to localize the esophagus in computed tomographic (CT) images. In this work we pre- sent a technique to automatically detect esophageal air holes in this modality. Our technique is based on the extraction of a volume of interest, air segmentation by thresholding and classification of respiratory and esophageal air using a priori knowledge about the connectivity of air voxels. A post-processing step rejects wrong results from artifacts in the CT image. We successfully tested our algorithm with clinical data and compared the detection results of a human expert and our technique.

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    ABSTRACT: Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial probability map generated from detected air. Threefold cross-validation on 144 datasets showed that this probability map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 1):95-102.
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    [Show abstract] [Hide abstract]
    ABSTRACT: Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial probability map generated from detected air. Threefold cross-validation on 144 datasets showed that this probability map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better. Additional material can be found at http://www5.informatik.uni-erlangen.de/fileadmin/Persons/FeulnerJohannes/RoiWithoutAndWithSegmentation.avi .
    09/2010: pages 95-102;
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    ABSTRACT: Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a “detect and connect” approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods.
    IEEE Transactions on Medical Imaging 07/2011; · 3.80 Impact Factor

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