TY - JOUR
T1 - On measuring the change in size of pulmonary nodules
AU - Reeves, Anthony P.
AU - Chan, Antoni B.
AU - Yankelevitz, David F.
AU - Henschke, Claudia I.
AU - Kressler, Bryan
AU - Kostis, William J.
N1 - Funding Information:
Manuscript received January 9, 2006; revised January 16, 2006. This work was supported in part by National Institutes of Health (NIH) under Grant R01-CA-63393, Grant R01-CA-78905, and Grant R33-CA-101110. Asterisk indicates corresponding author. *A. P. Reeves is with the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853 USA (e-mail: [email protected]). A. B. Chan and B. Kressler are with the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853 USA. D. F. Yankelevitz, C. I. Henschke, and W. J. Kostis are with the Joan and Sanford I. Weill Medical College, Cornell University, New York, NY 14853 USA. Digital Object Identifier 10.1109/TMI.2006.871548
PY - 2006/4
Y1 - 2006/4
N2 - The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p = 0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.
AB - The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p = 0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.
KW - Computed tomography
KW - Growth rate estimation
KW - Image registration
KW - Image segmentation
KW - Pulmonary nodules
KW - Rule-based segmentation.
UR - https://www.scopus.com/pages/publications/33645696746
U2 - 10.1109/TMI.2006.871548
DO - 10.1109/TMI.2006.871548
M3 - Article
C2 - 16608059
AN - SCOPUS:33645696746
SN - 0278-0062
VL - 25
SP - 435
EP - 450
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 4
ER -