A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

Mikael Agn; Ian Law; Per Munck af Rosenschöld; Koen Van Leemput

doi:10.1117/12.2216814

21 March 2016 A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

Mikael Agn, Ian Law, Per Munck af Rosenschöld, Koen Van Leemput

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841D (2016) https://doi.org/10.1117/12.2216814
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

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.

Citation Download Citation

Mikael Agn, Ian Law, Per Munck af Rosenschöld, and Koen Van Leemput "A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841D (21 March 2016); https://doi.org/10.1117/12.2216814

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