{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tang X"],"funding":["NIBIB NIH HHS","NIA NIH HHS","NCRR NIH HHS","NIMH NIH HHS"],"pagination":["e96985"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4014574"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(5)"],"pubmed_abstract":["In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8-0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images - an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure."],"journal":["PloS one"],"pubmed_title":["Multi-contrast multi-atlas parcellation of diffusion tensor imaging of the human brain."],"pmcid":["PMC4014574"],"funding_grant_id":["R01 MH084803","R01 EB000975","P41EB015909","R01 EB017638","P41 EB015909","S10 RR025053","R01 AG020012"],"pubmed_authors":["Li Y","Miller MI","Poretti A","Yoshida S","Hsu J","Huisman TA","Mori S","Oishi K","Kutten K","Faria AV","Tang X"],"additional_accession":[]},"is_claimable":false,"name":"Multi-contrast multi-atlas parcellation of diffusion tensor imaging of the human brain.","description":"In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8-0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images - an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.","dates":{"release":"2014-01-01T00:00:00Z","publication":"2014","modification":"2025-04-18T23:14:43.364Z","creation":"2019-03-26T23:24:43Z"},"accession":"S-EPMC4014574","cross_references":{"pubmed":["24809486"],"doi":["10.1371/journal.pone.0096985"]}}