Title :
Multi-contrast deep nuclei segmentation using a probabilistic atlas
Author :
Marrakchi-Kacem, Linda ; Poupon, Cyril ; Mangin, Jean-François ; Poupon, Fabrice
Author_Institution :
NeuroSpin, CEA, Gif-Sur-Yvette, France
Abstract :
In this paper we propose a new hybrid segmentation approach of the deep brain structures based on a multi-contrast 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.
Keywords :
biodiffusion; biomedical MRI; brain; deformation; image segmentation; medical image processing; FREESURFER; FSL-FIRST; MRI; T1-weighted data contrast; deep brain structures; diffusion tensor contrast; hybrid segmentation; morphological information; multi-contrast deep nuclei segmentation; multi-contrast deformable model; Anisotropic magnetoresistance; Brain; Clustering algorithms; Deformable models; Image segmentation; Magnetic resonance imaging; Shape; Spatial databases; Tensile stress; Topology; deep nuclei; deformable model; diffusion tensor; multi-contrast; probabilistic atlas; segmentation;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on
Conference_Location :
Rotterdam
Print_ISBN :
978-1-4244-4125-9
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2010.5490415