TY - GEN
T1 - Robust segmentation using non-parametric snakes with multiple cues for applications in radiation oncology
AU - Kalpathy-Cramer, Jayashree
AU - Ozertem, Umut
AU - Hersh, William
AU - Fuss, Martin
AU - Erdogmus, Deniz
PY - 2009
Y1 - 2009
N2 - Radiation therapy is one of the most effective treatments used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the perceived tumor while sparing the surrounding healthy tissues. Radiation oncologists often manually delineate normal and diseased structures on 3D-CT scans, a time consuming task. We present a segmentation algorithm using non-parametric snakes and principal curves that can be used in an automatic or semi-supervised fashion. It provides fast segmentation that is robust with respect to noisy edges and does not require the user to optimize a variety of parameters, unlike many segmentation algorithms. It allows multiple cues to be incorporated easily for the purposes of estimating the edge probability density. These cues, including texture, intensity and shape priors, can be used simultaneously to delineate tumors and normal anatomy, thereby increasing the robustness of the algorithm. The notion of principal curves is used to interpolate between data points in sparse areas. We compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures for tumors as well as normal organs.
AB - Radiation therapy is one of the most effective treatments used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the perceived tumor while sparing the surrounding healthy tissues. Radiation oncologists often manually delineate normal and diseased structures on 3D-CT scans, a time consuming task. We present a segmentation algorithm using non-parametric snakes and principal curves that can be used in an automatic or semi-supervised fashion. It provides fast segmentation that is robust with respect to noisy edges and does not require the user to optimize a variety of parameters, unlike many segmentation algorithms. It allows multiple cues to be incorporated easily for the purposes of estimating the edge probability density. These cues, including texture, intensity and shape priors, can be used simultaneously to delineate tumors and normal anatomy, thereby increasing the robustness of the algorithm. The notion of principal curves is used to interpolate between data points in sparse areas. We compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures for tumors as well as normal organs.
KW - Non-parametric snakes
KW - Principal curves
KW - Segmentation
KW - Tumors
UR - http://www.scopus.com/inward/record.url?scp=71649108693&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71649108693&partnerID=8YFLogxK
U2 - 10.1117/12.812712
DO - 10.1117/12.812712
M3 - Conference contribution
AN - SCOPUS:71649108693
SN - 9780819475107
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2009 - Image Processing
T2 - Medical Imaging 2009 - Image Processing
Y2 - 8 February 2009 through 10 February 2009
ER -