A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients
Mikael Agn, Ian G. Law, Per Munck af Rosenschöld, Koen Van Leemput · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method.