RT Conference Proceedings
SR 00
ID 10.1007/978-3-642-25719-3_8
A1 Yang, X.
A1 Beddoe, G.
A1 Slabaugh, G. G.
T1 Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model
YR 2010
FD 20-09-2010
SP 53 
OP  59
K1 Rectal Tube, RANSAC, CAD, CT colonography
AB The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, trained using kernel density estimation (KDE) on simple low-level features, are employed to rank and select the most likely RT tube candidate on each axial slice. Then, a shape model, robustly estimated using Random Sample Consensus (RANSAC), infers the global RT path from the selected local detections. Our method is validated using a diverse database, including data from five hospitals. The experiments demonstrate a high detection rate of the RT path, and when tested in a CAD system, reduce 20.3% of the FPs with no loss of CAD sensitivity.
NO The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25719-3_8
T2 Second International Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging
ED Beijing, China
AV Published
LK https://openaccess.city.ac.uk/id/eprint/4380/
UL http://dx.doi.org/10.1007/978-3-642-25719-3_8