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In this paper we present a method for simultaneously segmenting brain tumorsand an extensive set of organs-at-risk for radiation therapy planning ofglioblastomas. The method combines a contrast-adaptive generative model forwhole-brain segmentation with a new spatial regularization model of tumor shapeusing convolutional restricted Boltzmann machines. We demonstrateexperimentally that the method is able to adapt to image acquisitions thatdiffer substantially from any available training data, ensuring itsapplicability across treatment sites; that its tumor segmentation accuracy iscomparable to that of the current state of the art; and that it captures mostorgans-at-risk sufficiently well for radiation therapy planning purposes. Theproposed method may be a valuable step towards automating the delineation ofbrain tumors and organs-at-risk in glioblastoma patients undergoing radiationtherapy.

A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning