Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI.

Mark McIntosh,'s picture
PubMed URL:
Mori S
Author List: 
Wu D
Ma T
Ceritoglu C
Li Y
Chotiyanonta J
Hou Z
Hsu J
Xu X
Brown T
Miller MI
Mori S
PubMed ID: 
Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation.
Published Date: 
January, 2016

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