TY - GEN
T1 - Principal curve based semi-automatic segmentation of organs in 3D-CT
AU - You, S.
AU - Bas, E.
AU - Ataer-Cansizoglu, E.
AU - Kalpathy-Cramer, J.
AU - Erdogmus, Deniz
PY - 2011
Y1 - 2011
N2 - Radiation therapy plays an important and effective role in the treatment of cancer. A main goal in radiation therapy is to deliver high radiation doses to the perceived tumors while minimizing radiation to surrounding normal tissues. Manual delineation of tumors and organs-at-risk(OARs) on three-dimensional computed tomography (3D-CT) is both a time-consuming and labor intensive task, and there maybe variability between manual delineations by different radiation oncologists. In this paper, we present a semi-supervised method to segment the contours of organs represented by piecewise linear segments connected with a small number of points given the user's input in one or more slices as an approximate initialization. This method detects ridge samples from the kernel interpolation of the edge map and approximates the shape of organs using piecewise linear segments among those sample points based on the principal curve score. Results are provided in two 3D-CT scans. Evaluation of the efficacy of our semiautomatic segmentation method is based on the overlapping ratio between the manually delineated contours and the semiautomatic segmented contours represented by a small number of points. The preserved points can be as low as 10 percent of the initial manual points, and the Dice Coefficients are approximately 0.93 for lung segmentation.
AB - Radiation therapy plays an important and effective role in the treatment of cancer. A main goal in radiation therapy is to deliver high radiation doses to the perceived tumors while minimizing radiation to surrounding normal tissues. Manual delineation of tumors and organs-at-risk(OARs) on three-dimensional computed tomography (3D-CT) is both a time-consuming and labor intensive task, and there maybe variability between manual delineations by different radiation oncologists. In this paper, we present a semi-supervised method to segment the contours of organs represented by piecewise linear segments connected with a small number of points given the user's input in one or more slices as an approximate initialization. This method detects ridge samples from the kernel interpolation of the edge map and approximates the shape of organs using piecewise linear segments among those sample points based on the principal curve score. Results are provided in two 3D-CT scans. Evaluation of the efficacy of our semiautomatic segmentation method is based on the overlapping ratio between the manually delineated contours and the semiautomatic segmented contours represented by a small number of points. The preserved points can be as low as 10 percent of the initial manual points, and the Dice Coefficients are approximately 0.93 for lung segmentation.
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U2 - 10.1109/IEMBS.2011.6091536
DO - 10.1109/IEMBS.2011.6091536
M3 - Conference contribution
C2 - 22255760
AN - SCOPUS:84055189204
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6220
EP - 6223
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -