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Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape

  • Mikael Agn
  • Oula Puonti
  • Per Munck af Rosenschöld
  • Ian Law
  • Koen Van Leemput
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)

Abstract

In this paper, we present a fully automated generative method for brain tumor segmentation in multi-modal magnetic resonance images. The method is based on the type of generative model often used for segmenting healthy brain tissues, where tissues are modeled by Gaussian mixture models combined with a spatial atlas-based tissue prior. We extend this basic model with a tumor prior, which uses convolutional restricted Boltzmann machines (cRBMs) to model the shape of both tumor core and complete tumor, which includes edema and core. The cRBMs are trained on expert segmentations of training images, without the use of the intensity information in the training images. Experiments on public benchmark data of patients suffering from low- and high-grade gliomas show that the method performs well compared to current state-of-the-art methods, while not being tied to any specific imaging protocol.

Keywords

Markov Chain Monte Carlo Gaussian Mixture Model Tumor Core Tumor Segmentation Visible Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported by NIH NCRR (P41-RR14075), NIBIB (R01EB013565) and the Lundbeck Foundation (R141-2013-13117).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mikael Agn
    • 1
  • Oula Puonti
    • 1
  • Per Munck af Rosenschöld
    • 2
  • Ian Law
    • 3
  • Koen Van Leemput
    • 1
    • 4
  1. 1.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
  2. 2.Department of Oncology, RigshospitaletCopenhagen University HospitalCopenhagenDenmark
  3. 3.Department of Clinical Physiology, Nuclear Medicine and PET, RigshospitaletCopenhagen University HospitalCopenhagenDenmark
  4. 4.Martinos Center for Biomedical Imaging, MGHHarvard Medical SchoolBostonUSA

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