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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)532-540
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
StatePublished - 2011
Externally publishedYes

Fingerprint

Tumor Growth
Atlas
Growth Model
Registration
Labels
Tumors
Brain
Segmentation
Tissue
Posterior Probability
Magnetic resonance
Magnetic Resonance
Expectation Maximization
Expectation-maximization Algorithm
Modality
Tumor
Estimate

Keywords

  • diffusion-reaction model
  • EM
  • joint segmentation-registration

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Joint segmentation and deformable registration of brain scans guided by a tumor growth model. / Gooya, Ali; Pohl, Kilian M.; Bilello, Michel; Biros, George; Davatzikos, Christos.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6892 LNCS, No. PART 2, 2011, p. 532-540.

Research output: Contribution to journalArticle

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