Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

• Published in: NeuroImage (Orlando, Fla.) Vol. 46; no. 3; pp. 726 - 738
• Main Authors: Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2009; Elsevier Limited
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Brain - anatomy & histology; Brain research; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Methods; MRI; Pattern Recognition, Automated - methods; Propagation; Registration; Reproducibility of Results; Segmentation; Selection; Sensitivity and Specificity; Studies; Subtraction Technique; Segmentation; MRI; Selection; Registration; Atlases
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.02.018

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.