Learning-based prostate localization for image guided radiation therapy

Authors

Luping Zhou, University of WollongongFollow
Shu Liao, University of North Carolina
Wei Li, University of North Carolina
Dinggang Shen, University of North Carolina at Chapel HillFollow

RIS ID

85720

Publication Details

Zhou, L., Liao, S., Li, W. & Shen, D. (2011). Learning-based prostate localization for image guided radiation therapy. 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 2103-2106). Chicago, United States:

Abstract

Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions, thus filtering out features that are salient in image appearance, but irrelevant to prostate localization. Furthermore, an image similarity function is learned to make the image appearance distance consistent with the underlying prostate alignment. The efficacy of our proposed method has been demonstrated by the experiment.