Name
Abstract | ||
|---|---|---|
Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease on brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived features to first segment and then parcellate the thalamus. We incorporate fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features. Combining these multiple information sources allows us to identify discriminating dimensions and thus parcellate the thalamic nuclei. A hierarchical random forest framework with a multidimensional feature per voxel, first distinguishes thalamus from background, and then separates each group of thalamic nuclei. Using a leave one out cross-validation on 12 subjects we have a mean Dice score of 0.805 and 0.799 for the left and right thalami, respectively. We also report overlap for the thalamic nuclear groups. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10443-0_22 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Brain imaging,diffusion MRI,magnetic resonance imaging,machine learning,segmentation,thalamus parcellation | Voxel,Thalamus,Computer vision,Diffusion MRI,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Neuroimaging,Random forest,Modal,Magnetic resonance imaging | Conference |
Volume | Issue | ISSN |
8675 | Pt 3 | 0302-9743 |
Citations | PageRank | References |
4 | 0.42 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joshua Stough | 1 | 24 | 3.72 |
| Jeffrey Glaister | 2 | 20 | 3.46 |
| Chuyang Ye | 3 | 61 | 11.12 |
| Sarah H. Ying | 4 | 41 | 5.00 |
| Jerry L. Prince | 5 | 4990 | 488.42 |
| Aaron Carass | 6 | 383 | 43.15 |