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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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.

Original languageEnglish
Title of host publicationMedical Imaging 2016
Subtitle of host publicationImage Processing
EditorsMartin A. Styner, Elsa D. Angelini
PublisherSPIE
ISBN (Electronic)9781510600195
DOIs
Publication statusPublished - 2016 Jan 1
Externally publishedYes
EventMedical Imaging 2016: Image Processing - San Diego, United States
Duration: 2016 Mar 12016 Mar 3

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9784
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2016: Image Processing
Country/TerritoryUnited States
CitySan Diego
Period2016/03/012016/03/03

Subject classification (UKÄ)

  • Cancer and Oncology
  • Medical Image Processing

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