Cortical Surface Parcellation via dMRI Using Mutual Nearest Neighbor Condition

Brahim Belaoucha (1, 2) , Maureen Clerc (2, 1) , Théodore Papadopoulo (2, 1)

1 UNS - Université Nice Sophia Antipolis (1965 - 2019)
2 ATHENA - Computational Imaging of the Central Nervous System

In this paper, we present a method that aims at parcellating the cortical surface from individual anatomy. The parcellation is obtained using the Mutual Nearest Neighbor (MNN) criterion to obtain regions with similar structural connectivity. The structural connectivity is obtained by applying a probabilis-tic tractography on the diffusion MRI (dMRI), a non-invasive modality allowing access to the structural information of the white matter. The results are compared to some of the atlases that can be found in the literature. We show that these atlases have lower similarity of structural connectivity than the proposed algorithm implying that the regions of the atlases may have lower functional homogeneity.

Mots clés: Parcellation; dMRI; Mutual Nearest Neighbor

Domaines: Neurosciences; Informatique [cs]

Brahim Belaoucha, Maureen Clerc, Théodore Papadopoulo. Cortical Surface Parcellation via dMRI Using Mutual Nearest Neighbor Condition. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Apr 2016, Prague, Czech Republic. pp.903--906. ⟨hal-01306239⟩