TY - JOUR
T1 - Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy
AU - Graham, Michael W.
AU - Gibbs, Jason D.
AU - Cornish, Duane C.
AU - Higgins, William E.
N1 - Funding Information:
Manuscript received July 28, 2009; revised October 17, 2009; accepted October 21, 2009. First published March 22, 2010; current version published April 02, 2010. This work was supported by the National Institutes of Health under the National Cancer Institute Grant R01-CA074325 and Grant R44-CA091534. Asterisk indicates corresponding author. M. W. Graham is with Google, Inc., Pittsburgh, PA 15213 USA. J. D. Gibbs is with Broncus Technologies, State College, PA 16801 USA. D. C. Cornish is with the Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802 USA. *W. E. Higgins is with the Departments of Electrical Engineering, Computer Science and Engineering, and Bioengineering, Pennsylvania State University, University Park, PA 16802 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2009.2035813
PY - 2010/4
Y1 - 2010/4
N2 - A vital task in the planning of peripheral bronchoscopy is the segmentation of the airway tree from a 3-D multidetector computed tomography chest scan. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. We present a robust method that draws upon both local and global information. The method begins with a conservative segmentation of the major airways. Follow-on stages then exhaustively search for additional candidate airway locations. Finally, a graph-based optimization method counterbalances both the benefit and cost of retaining candidate airway locations for the final segmentation. Results demonstrate that the proposed method typically extracts 23 more generations of airways than several other methods, and that the extracted airway trees enable image-guided bronchoscopy deeper into the human lung periphery than past studies.
AB - A vital task in the planning of peripheral bronchoscopy is the segmentation of the airway tree from a 3-D multidetector computed tomography chest scan. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. We present a robust method that draws upon both local and global information. The method begins with a conservative segmentation of the major airways. Follow-on stages then exhaustively search for additional candidate airway locations. Finally, a graph-based optimization method counterbalances both the benefit and cost of retaining candidate airway locations for the final segmentation. Results demonstrate that the proposed method typically extracts 23 more generations of airways than several other methods, and that the extracted airway trees enable image-guided bronchoscopy deeper into the human lung periphery than past studies.
UR - https://www.scopus.com/pages/publications/77950385866
UR - https://www.scopus.com/pages/publications/77950385866#tab=citedBy
U2 - 10.1109/TMI.2009.2035813
DO - 10.1109/TMI.2009.2035813
M3 - Article
C2 - 20335095
AN - SCOPUS:77950385866
SN - 0278-0062
VL - 29
SP - 982
EP - 997
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 4
M1 - 5437330
ER -