Hierarchical Part-Based Detection of 3D Flexible Tubes: Application to CT Colonoscopy

  • Adrian Barbu
  • Luca Bogoni
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


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.


False Alarm False Alarm Rate Dynamic Programming Algorithm Manual Annotation Vote Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bernstein, E., Amit, Y.: Part-based statistical models for object classification and detection. In: CVPR (2005)Google Scholar
  2. 2.
    Iordanescu, G., Summers, R.: Reduction of false positives on the rectal tube in computer-aided detection for CT colonography. Med Phys 31(10), 2855–2862 (2004)CrossRefGoogle Scholar
  3. 3.
    Schapire, R.E.: The boosting approach to machine learning: An overview. In: Denison, D.D., Hansen, M.H., Holmes, C., Mallick, B., Yu, B. (eds.) Nonlinear Estimation and Classification, Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Suzuki, K., Yoshida, H., Nappi, J.J., Armato, S.G., Dachman, A.H.: False-positive Reduction in Computer-aided Detection of Polyps in CT Colonography Based on Multiple Massive Training Artificial Neural Networks. In: RSNA (2005)Google Scholar
  5. 5.
    Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: ICCV (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Adrian Barbu
    • 1
  • Luca Bogoni
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Siemens Corporate ResearchPrinceton
  2. 2.Siemens CADMalvern

Personalised recommendations