@inproceedings{872a7f244ae5418987fd4ada085f1d53,
title = "A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients",
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.",
author = "Mikael Agn and Ian Law and {Munck Af Rosensch{\"o}ld}, Per and {Van Leemput}, Koen",
year = "2016",
month = jan,
day = "1",
doi = "10.1117/12.2216814",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Styner, {Martin A.} and Angelini, {Elsa D.}",
booktitle = "Medical Imaging 2016",
address = "United States",
note = "Medical Imaging 2016: Image Processing ; Conference date: 01-03-2016 Through 03-03-2016",
}