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.
Klinder
Tobias
T
Institut für Informationsverarbeitung, University of Hannover, Germany. klinder@tnt.uni-hannover.de
Lorenz
Cristian
C
von Berg
Jens
J
Dries
Sebastian P M
SP
Bülow
Thomas
T
Ostermann
Jörn
J
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*