TY - GEN
T1 - Learning methods for lung tumor markerless gating in image-guided radiotherapy
AU - Cui, Ying
AU - Dy, Jennifer G.
AU - Sharp, Gregory C.
AU - Alexander, Brian M.
AU - Jiang, Steve B.
PY - 2008
Y1 - 2008
N2 - In an idealized gated radiotherapy treatment, radiation is delivered only when the tumor is at the right position. For gated lung cancer radiotherapy, it is difficult to generate accurate gating signals due to the large uncertainties when using external surrogates and the risk of pneumothorax when using implanted fiducial markers. In this paper, we investigate machine learning algorithms for markerless gated radiotherapy with fluoroscopic images. Previous approach utilizes template matching to localize the tumor position. Here, we investigate two ways to improve the precision of tumor target localization by applying: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. Template matching only considers images inside the gating window, but images outside the gating window might provide additional information. We take advantage of both states and re-cast the gating problem into a classification problem. Thus, we are able to use the SVM classifier for gated radiotherapy. To verify the effectiveness of the two proposed techniques, we apply them on five sequences of fluoroscopic images from five lung cancer patients against the gating signal of manually contoured tumors as ground truth. Our five-patient case study shows that both ensemble template matching and SVM are reasonable tools for image-guided markerless gated radiotherapy with an average of approximately 95% precision in terms of delivered target dose at approximately 35% duty cycle.
AB - In an idealized gated radiotherapy treatment, radiation is delivered only when the tumor is at the right position. For gated lung cancer radiotherapy, it is difficult to generate accurate gating signals due to the large uncertainties when using external surrogates and the risk of pneumothorax when using implanted fiducial markers. In this paper, we investigate machine learning algorithms for markerless gated radiotherapy with fluoroscopic images. Previous approach utilizes template matching to localize the tumor position. Here, we investigate two ways to improve the precision of tumor target localization by applying: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. Template matching only considers images inside the gating window, but images outside the gating window might provide additional information. We take advantage of both states and re-cast the gating problem into a classification problem. Thus, we are able to use the SVM classifier for gated radiotherapy. To verify the effectiveness of the two proposed techniques, we apply them on five sequences of fluoroscopic images from five lung cancer patients against the gating signal of manually contoured tumors as ground truth. Our five-patient case study shows that both ensemble template matching and SVM are reasonable tools for image-guided markerless gated radiotherapy with an average of approximately 95% precision in terms of delivered target dose at approximately 35% duty cycle.
KW - Applied machine learning
KW - Classification
KW - Clustering
KW - Image-guided radiotherapy
KW - Mixture model
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=65449131274&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65449131274&partnerID=8YFLogxK
U2 - 10.1145/1401890.1401998
DO - 10.1145/1401890.1401998
M3 - Conference contribution
AN - SCOPUS:65449131274
SN - 9781605581934
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 902
EP - 910
BT - KDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
T2 - 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
Y2 - 24 August 2008 through 27 August 2008
ER -