Atlas selection strategy using least angle regression in multi-atlas segmentation propagation

Shen, Kaikai, Bourgeat, Pierrick, Dowson, Nicholas, Meriaudeau, Fabrice, Salvado, Olivier and Alzheimer’s Disease Neuroimaging Initiative (2011). Atlas selection strategy using least angle regression in multi-atlas segmentation propagation. In: 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 8th International Symposium on Biomedical Imaging (ISBI’11), Chicago, IL, United States, (1746-1749). 30 March - 2 April 2011. doi:10.1109/ISBI.2011.5872743


Author Shen, Kaikai
Bourgeat, Pierrick
Dowson, Nicholas
Meriaudeau, Fabrice
Salvado, Olivier
Alzheimer’s Disease Neuroimaging Initiative
Title of paper Atlas selection strategy using least angle regression in multi-atlas segmentation propagation
Conference name 8th International Symposium on Biomedical Imaging (ISBI’11)
Conference location Chicago, IL, United States
Conference dates 30 March - 2 April 2011
Proceedings title 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings   Check publisher's open access policy
Journal name 2011 8th Ieee International Symposium On Biomedical Imaging: From Nano to Macro   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/ISBI.2011.5872743
ISBN 9781424441280
ISSN 1945-7928
Start page 1746
End page 1749
Total pages 4
Collection year 2012
Language eng
Abstract/Summary In multi-atlas based segmentation propagation, segmentations from multiple atlases are propagated to the target image and combined to produce the segmentation result. Local weighted voting (LWV) method is a classifier fusion method which combines the propagated atlases weighted by local image similarity. We demonstrate that the segmentation accuracy using LWV improves as the number of atlases increases. Under this context, we show that introducing diversity in addition to image similarity by using least-angle regression (LAR) criteria is a more efficient way to rank and select atlases. The accuracy of multi-atlas segmentation converges faster when the atlases are selected in the order of LAR. We test the method on a hippocampal atlas set of 138 normal control (NC) subjects and another set of 99 Alzheimer's disease patients provided by ADNI. The result shows that LAR selection is more efficient than similarity based atlas selection. Fewer atlases were required using LAR selected atlases to achieve the same accuracy as fusing atlases from image similarity based selection.
Keyword MRI
Image segmentation
Multi-atlas segmentation propagation
Atlas selection
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status Non-UQ

 
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