We present the application of deep multi-class classifiers for registration of the pre-radiation image (CBCT) to the treatment planning image (planCT) in Radiation Therapy (RT). We train a multi-class classifier on different classes of displacement between 3D patches of images and use it for registration. As the initial displacement between images might be large, we train multiple classifiers for different resolutions of the data to capture larger displacements in coarser resolutions. We show that having only a few patients, the deep multi-class classifiers enable an accurate and fast rigid registration for CBCT to planCT even with significantly different fields of view. Our work lays the foundation for deformable image registration and prediction of registration uncertainty which can be utilized for adaptive RT.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.