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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)

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.

Keywords

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.

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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

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