Name
Abstract | ||
|---|---|---|
The thalamus sub-cortical gray matter structure consists of contiguous nuclei, each individually responsible for communication between various cerebral cortex and midbrain regions. These nuclei are differentially affected in neurodegenerative diseases such as multiple sclerosis and Alzheimer's. However thalamic parcellation of the nuclei, manual or automatic, is difficult given the limited contrast in any particular magnetic resonance (MR) modality. Several groups have had qualitative success differentiating nuclei based on spatial location and fiber orientation information in diffusion tensor imaging (DTI). In this paper, we extend these principles by combining these discriminating dimensions with structural MR and derived information, and by building random forest learners on the resultant multi-modal features. In training, we form a multi-dimensional feature per voxel, which we associate with a nucleus classification from a manual rater. Learners are trained to differentiate thalamus from background and thalamic nuclei from other nuclei. These learners inform the external forces of a multiple object level set model. Our cross-validated quantitative results on a set of twenty subjects show the efficacy and reproducibility of our results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISBI.2013.6556609 | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging |
Keywords | Field | DocType |
multiple sclerosis,magnetic resonance modality,diseases,multiple object level set model,spatial location,neurophysiology,midbrain regions,biodiffusion,learning (artificial intelligence),neurodegenerative diseases,mri,nucleus classification,cross-validated quantitative results,random forests,random forest learning,object segmentation,thalamic parcellation,thalamus subcortical gray matter structure,diffusion tensor imaging,multidimensional feature per voxel,alzheimer disease,contiguous nuclei,biomedical mri,resultant multimodal features,feature extraction,image classification,deformable models,brain,multimodal data,Diffusion tensor imaging,fiber orientation information,machine learning,medical image processing,cerebral cortex | Thalamus,Voxel,Computer vision,Diffusion MRI,Nucleus,Pattern recognition,Neurophysiology,Computer science,Feature extraction,Artificial intelligence,Random forest,Contextual image classification | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4673-6456-0 | 5 |
PageRank | References | Authors |
0.48 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joshua Stough | 1 | 24 | 3.72 |
| Chuyang Ye | 2 | 61 | 11.12 |
| Sarah H. Ying | 3 | 41 | 5.00 |
| Jerry L. Prince | 4 | 4990 | 488.42 |