Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model

  • Ali Gooya
  • Kilian M. Pohl
  • Michel Bilello
  • George Biros
  • Christos Davatzikos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)


This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR ) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth.


joint segmentation-registration EM diffusion-reaction model 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ali Gooya
    • 1
  • Kilian M. Pohl
    • 1
  • Michel Bilello
    • 1
  • George Biros
    • 1
  • Christos Davatzikos
    • 1
  1. 1.Section for Biomedical Image AnalysisPhiladelphiaUS

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