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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Barbu, A., Bogoni, L., Comaniciu, D. (2006). Hierarchical Part-Based Detection of 3D Flexible Tubes: Application to CT Colonoscopy. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_57
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DOI: https://doi.org/10.1007/11866763_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44727-6
Online ISBN: 978-3-540-44728-3
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