Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model
نویسندگان
چکیده
The rectal tube (RT) is a common source of false positives (FPs) in computeraided 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
منابع مشابه
A robust and efficient approach to detect 3D rectal tubes from CT colonography.
PURPOSE The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. A robust and efficient detection of RT can improve CAD performance by eliminating such "obvious" FPs and increase radiologists' confidence in CAD. METHODS In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, ...
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