GLISTR: Glioma image segmentation and registration

Ali Gooya, Kilian M. Pohl, Michel Bilello, Luigi Cirillo, George Biros, Elias R. Melhem, Christos Davatzikos

Research output: Contribution to journalArticle

Abstract

We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. 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 original atlas into one with tumor and edema adapted to best match a given set of patient's images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.

Original languageEnglish (US)
Article number6266750
Pages (from-to)1941-1954
Number of pages14
JournalIEEE transactions on medical imaging
Volume31
Issue number10
DOIs
StatePublished - Oct 9 2012

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Keywords

  • Diffusion-reaction model
  • expectation maximization (EM) algorithm
  • glioma atlas
  • joint segmentation-registration

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Gooya, A., Pohl, K. M., Bilello, M., Cirillo, L., Biros, G., Melhem, E. R., & Davatzikos, C. (2012). GLISTR: Glioma image segmentation and registration. IEEE transactions on medical imaging, 31(10), 1941-1954. [6266750]. https://doi.org/10.1109/TMI.2012.2210558