, Volume 11, Issue 4, pp 447–468

Deformable Templates Guided Discriminative Models for Robust 3D Brain MRI Segmentation


    • Laboratory of Neuro Imaging Department of NeurologyUCLA School of Medicine
  • Juan Eugenio Iglesias
    • Department of RadiologyMassachusetts General Hospital
  • Zhuowen Tu
    • Laboratory of Neuro Imaging Department of NeurologyUCLA School of Medicine
  • for The Alzheimer’s Disease Neuroimaging Initiative
Original Article

DOI: 10.1007/s12021-013-9190-5

Cite this article as:
Liu, C., Iglesias, J.E., Tu, Z. et al. Neuroinform (2013) 11: 447. doi:10.1007/s12021-013-9190-5


Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.


Brain image segmentationFusionDeformable templatesDiscriminative modelsGenerative models

Supplementary material

12021_2013_9190_MOESM1_ESM.doc (206 kb)
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Copyright information

© Springer Science+Business Media New York 2013