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Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): Validation on hippocampus segmentation

Date

Authors

Khan, Ali R.

Cherbuin, Nicolas

Wen, Wei

Sachdev, Perminder Singh

Beg, Mirza Faizal

Anstey, Kaarin

Publisher

Academic Press

Abstract

We developed a novel method for spatially-local selection of atlas-weights in multi-atlas segmentation that combines supervised learning on a training set and dynamic information in the form of local registration accuracy estimates (SuperDyn). Supervised

Keywords

Keywords: adult; aged; article; classifier; clinical article; cognitive defect; controlled study; female; hippocampus; human; image analysis; image processing; intermethod comparison; male; neuroimaging; nuclear magnetic resonance imaging; priority journal; statist Classifier combination; Hippocampus; MRI; Segmentation; Segmentation validation; Weighted voting

URI

http://hdl.handle.net/1885/66341

Collections

ANU Research Publications

Source

Neuroimage

Type

Journal article

DOI

10.1016/j.neuroimage.2011.01.078

Restricted until

2037-12-31

Downloads

File

Description

01_Khan_Optimal_weights_for_local_2011.pdf (1.93 MB)

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