Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging


  • Marius George Linguraru EPIDAURE/ASCLEPIOS Research Group, INRIA Sophia Antipolis, France Division of Engineering and Applied Sciences Harvard University, Cambridge MA, USA E-mail:
  • Miguel íngel Gonzí¡lez Ballester University of Bern, Bern, Switzerland MEM Research Center, Institute for Surgical Technology and Biomechanics
  • Nicholas Ayache EPIDAURE/ASCLEPIOS Research Group, INRIA Sophia Antipolis, France


MRI, brain, deep grey matter nuclei, atlas, image normalisation, registration, segmentation


Magnetic resonance imaging (MRI) is commonly employed for the depiction of soft tissues, most notably the human brain. Computer-aided image analysis techniques lead to image enhancement and automatic detection of anatomical structures. However, the information contained in images does not often offer enough contrast to robustly obtain a good detection of all internal brain structures, not least the deep grey matter nuclei. We propose a method that incorporates prior anatomical knowledge in the shape of digital atlases that deform to fit the image data to be analysed. Our technique is based on a combination of rigid, affine and non-rigid registration, segmentation of key anatomical landmarks and propagation of the information of the atlas to detect deep grey matter nuclei. The Montreal Neurological Institute (MNI) and Zubal atlases are employed. Results show that detecting important structures such as the ventricles and brain outlines greatly improves the results. Our method is fully automatic.


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