  In this paper we present a method for simultaneously segmenting brain tumors
and an extensive set of organs-at-risk for radiation therapy planning of
glioblastomas. The method combines a contrast-adaptive generative model for
whole-brain segmentation with a new spatial regularization model of tumor shape
using convolutional restricted Boltzmann machines. We demonstrate
experimentally that the method is able to adapt to image acquisitions that
differ substantially from any available training data, ensuring its
applicability across treatment sites; that its tumor segmentation accuracy is
comparable to that of the current state of the art; and that it captures most
organs-at-risk sufficiently well for radiation therapy planning purposes. The
proposed method may be a valuable step towards automating the delineation of
brain tumors and organs-at-risk in glioblastoma patients undergoing radiation
therapy.
