We present a new model-based approach for an automated labeling and segmentation of the rib cage in chest CT scans. A mean rib cage model including a complete vertebral column is created out of 29 data sets. We developed a ray search based procedure for rib cage detection and initial model pose. After positioning the model, it was adapted to 18 unseen CT data. In 16 out of 18 data sets, detection, labeling, and segmentation succeeded with a mean segmentation error of less than 1.3 mm between true and detected object surface. In one case the rib cage detection failed, in another case the automated labeling. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Klinder, T., Lorenz, C., Von Berg, J., Dries, S. P. M., Bülow, T., & Ostermann, J. (2007). Automated model-based rib cage segmentation and labeling in CT images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4792 LNCS, pp. 195–202). Springer Verlag. https://doi.org/10.1007/978-3-540-75759-7_24
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