TY - GEN
T1 - Semi-supervised segmentation using non-parametric snakes for 3D-CT applications in radiation oncology
AU - Kalpathy-Cramer, Jayashree
AU - Ozertem, Umut
AU - Hersh, William
AU - Fuss, Martin
AU - Erdogmus, Deniz
PY - 2008
Y1 - 2008
N2 - We present a semi-supervised protocol for segmentation of tumors and normal anatomy for applications in Radiation Oncology. A primary goal in radiation therapy in oncology is to deliver high radiation dose to the perceived tumor while sparing the surrounding non-diseased organs. Consequently, a critical task in the workflow of radiation oncologists is the manual delineation of normal and diseased structures on 3D-CT scans. In this paper, we compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures. Structures include tumors as well as normal organs including lungs, liver and kidneys. This technique provides fast segmentation that is robust with respect to noisy edges. In addition, this algorithm does not require the user to optimize a variety of parameters unlike many segmentation algorithms. We provide results that show the improvement in overlap between the manually delineated gold standard and the output of the segmentation algorithms using the user input.
AB - We present a semi-supervised protocol for segmentation of tumors and normal anatomy for applications in Radiation Oncology. A primary goal in radiation therapy in oncology is to deliver high radiation dose to the perceived tumor while sparing the surrounding non-diseased organs. Consequently, a critical task in the workflow of radiation oncologists is the manual delineation of normal and diseased structures on 3D-CT scans. In this paper, we compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures. Structures include tumors as well as normal organs including lungs, liver and kidneys. This technique provides fast segmentation that is robust with respect to noisy edges. In addition, this algorithm does not require the user to optimize a variety of parameters unlike many segmentation algorithms. We provide results that show the improvement in overlap between the manually delineated gold standard and the output of the segmentation algorithms using the user input.
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U2 - 10.1109/MLSP.2008.4685464
DO - 10.1109/MLSP.2008.4685464
M3 - Conference contribution
AN - SCOPUS:58049163686
SN - 9781424423767
T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
SP - 109
EP - 114
BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Y2 - 16 October 2008 through 19 October 2008
ER -