Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration

Stefan Bauer*, Christof Seiler, Thibaut Bardyn, Philippe Buechler, Mauricio Reyes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

29 Citations (Web of Science)


We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Original languageEnglish
Title of host publicationIEEE Engineering in Medicine and Biology Society
Number of pages4
Publication statusPublished - 2010
Externally publishedYes

Publication series

SeriesIEEE Engineering in Medicine and Biology Society


  • Atlases as Topic
  • Humans
  • Magnetic Resonance Imaging
  • Markov Chains
  • Models, Biological
  • Neoplasms/pathology

Cite this