Efficient multilevel brain tumor segmentation with integrated bayesian model classification

Jason J. Corso, Eitan Sharon, Shishir Dube, Suzie El-Saden, Usha Sinha, Alan Yuille

Research output: Contribution to journalArticlepeer-review

392 Scopus citations

Abstract

We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.

Original languageEnglish
Article number4384610
Pages (from-to)629-640
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume27
Issue number5
DOIs
StatePublished - May 2008

Keywords

  • Bayesian affinity
  • Brain tumor
  • Glioblastoma multiforme
  • Multilevel segmentation
  • Normalized cuts

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