title: Hierarchical part-based detection of 3D flexible tubes: application to CT colonoscopy.
abstract: In this paper, we present a learning-based method for the detection and segmentation of 3D free-form tubular structures, such as the rectal tubes in CT colonoscopy. This method can be used to reduce the false alarms introduced by rectal tubes in current polyp detection algorithms. The method is hierarchical, detecting parts of the tube in increasing order of complexity, from tube cross sections and tube segments to the whole flexible tube. To increase the speed of the algorithm, candidate parts are generated using a voting strategy. The detected tube segments are combined into a flexible tube using a dynamic programming algorithm. Testing the algorithm on 210 unseen datasets resulted in a tube detection rate of 94.7% and 0.12 false alarms per volume. The method can be easily retrained to detect and segment other tubular 3D structures.
authors.affiliation: Siemens Corporate Research, Princeton, NJ, USA.
authors.forename: Adrian
authors.initials: A
authors.lastname: Barbu
authors.forename: Luca
authors.initials: L
authors.lastname: Bogoni
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D023881:Colonography, Computed Tomographic / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D019736:Prostheses and Implants; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012007:Rectum / Q000000981:diagnostic imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique