Name
Title | ||
|---|---|---|
Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber-clustering method. |
Abstract | ||
|---|---|---|
We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type in each dataset. We then created a fiber bundle template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TVCG.2008.52 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
clustering result,automated proximity-based clustering,fiber bundle,identifying white-matter fiber bundles,pattern clustering,fiber bundle template,index terms— cluster,automated proximity-based fiber-clustering method,gray matter,white matter fiber bundle identification,expert rater,mri,diffusion tensor imaging,clustering method,cluster,biomedical mri,dti data,target fiber bundle,dt-mri,study neural fiber bundle,optimal clustering,brain,dti,anatomically plausible fiber bundle,dt- mri,medical image processing,clustering,fiber clustering method,clustering algorithms,labeling,robustness,indexing terms,magnetic resonance imaging,sampling methods,anatomy | Computer vision,Diffusion MRI,White matter,Computer science,Fiber clustering,Robustness (computer science),Artificial intelligence,Sampling (statistics),Cluster analysis,Bundle,Fiber bundle | Journal |
Volume | Issue | ISSN |
14 | 5 | 1077-2626 |
Citations | PageRank | References |
49 | 2.11 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Zhang | 1 | 642 | 53.89 |
| Stephen Correia | 2 | 88 | 5.07 |
| David H. Laidlaw | 3 | 1781 | 234.58 |