Name
Title | ||
|---|---|---|
Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. |
Abstract | ||
|---|---|---|
Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neuroimage.2009.02.018 | NeuroImage |
Keywords | Field | DocType |
MRI,Segmentation,Selection,Atlases,Registration | Data mining,Pattern recognition,Ranking,Computer science,Segmentation,Image segmentation,Selection criterion,Atlas (anatomy),Artificial intelligence,Neuroimaging,Dice,Atlases as Topic | Journal |
Volume | Issue | ISSN |
46 | 3 | 1053-8119 |
Citations | PageRank | References |
291 | 9.78 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P Aljabar | 1 | 393 | 19.19 |
| R A Heckemann | 2 | 292 | 10.47 |
| A Hammers | 3 | 399 | 18.98 |
| J. V. Hajnal | 4 | 359 | 17.44 |
| Daniel Rueckert | 5 | 9338 | 637.58 |