Learning methods for lung tumor markerless gating in image-guided radiotherapy

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 th...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian M. Alexander, Steve B. Jiang
• Keywords: Accurate Gating Signals, Data Mining, Idealized Gated Radiotherapy, KDD 2008, Markerless Gated Radiotherapy