A supervised framework for the registration and segmentation of white matter fiber tracts

Arnaldo Mayer, Gali Zimmerman-Moreno, Ran Shadmi, Amit Batikoff, Hayit Greenspan

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method.

Original languageEnglish
Article number5549914
Pages (from-to)131-145
Number of pages15
JournalIEEE Transactions on Medical Imaging
Issue number1
StatePublished - Jan 2011
Externally publishedYes


  • Brain
  • registration
  • segmentation
  • tractography
  • white matter fiber tracts


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