Automated model-based rib cage segmentation and labeling in CT images.
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.
Institut für Informationsverarbeitung, University of Hannover, Germany. klinder@tnt.uni-hannover.de
Tobias
T
Klinder
Cristian
C
Lorenz
Jens
J
von Berg
Sebastian P M
SP
Dries
Thomas
T
Bülow
Jörn
J
Ostermann
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012272:Ribs / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*