Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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In this paper we propose a new hybrid segmentation approach of the deep brain structures based on a multicontrast deformable model of regions in competition, with deformations preserving the topology of the structures, as well as their shape and position, using a probabilistic atlas and some prior morphological information. The accuracy of our method was evaluated by comparing the results obtained on a base of T1-weighted data contrast with those of FREESURFER and FSL-FIRST. Besides giving very good results from only one contrast, we show that the multi-contrast aspect of our method allows exploiting the complementary contributions of different contrasts, like T1 and diffusion tensor (DT) contrasts, in order to provide a more robust segmentation.