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A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequences

  • Esther AlbertsEmail author
  • Guillaume Charpiat
  • Yuliya Tarabalka
  • Thomas Huber
  • Marc-André Weber
  • Jan Bauer
  • Claus Zimmer
  • Bjoern H. Menze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)

Abstract

Brain tumor segmentation and brain tumor growth assessment are inter-dependent and benefit from a joint evaluation. Starting from a generative model for multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the different image modalities. In this study, we validate this model to obtain brain tumor segmentations and volumetry in longitudinal image data. Moreover, we use the model to develop a probabilistic framework for estimating the likelihood of disease progression, i.e. tumor regrowth, after therapy. We present experiments for longitudinal image sequences with Open image in new window , Open image in new window , Open image in new window and flair images, acquired for ten patients with low and high grade gliomas.

Keywords

Energy Function Conditional Random Field Tumor Regrowth Tumor Segmentation Pairwise Potential 
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.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Esther Alberts
    • 1
    • 2
    • 6
    Email author
  • Guillaume Charpiat
    • 3
  • Yuliya Tarabalka
    • 4
  • Thomas Huber
    • 1
  • Marc-André Weber
    • 5
  • Jan Bauer
    • 1
  • Claus Zimmer
    • 1
  • Bjoern H. Menze
    • 2
    • 6
  1. 1.Neuroradiology, Klinikum Rechts der IsarTU MünchenMunichGermany
  2. 2.Department of Computer ScienceTU MünchenMunichGermany
  3. 3.TAO Research ProjectInria SaclayPalaiseauFrance
  4. 4.Titane Research ProjectInria Sophia-AntipolisValbonneFrance
  5. 5.Diagnostic and Interventional RadiologyUniversity of HeidelbergHeidelbergGermany
  6. 6.Institute for Advanced StudyTU MünchenMunichGermany

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