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

Dan Wu, Ting Ma, Can Ceritoglu, Yue Li, Jill Chotiyanonta, Zhipeng Hou, John Hsu, Xin Xu, Timothy Brown, Michael I. Miller, Susumu Mori

Research output: Contribution to journalArticle

Abstract

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 4-82. years 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.

Original languageEnglish (US)
Pages (from-to)120-130
Number of pages11
JournalNeuroImage
Volume125
DOIs
StatePublished - Jan 15 2016

Keywords

  • Atlas creation strategy
  • Dynamic age-matching
  • Hierarchical ontology
  • Multi-atlas
  • Segmentation accuracy
  • T1-weighted

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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    Wu, D., Ma, T., Ceritoglu, C., Li, Y., Chotiyanonta, J., Hou, Z., Hsu, J., Xu, X., Brown, T., Miller, M. I., & Mori, S. (2016). Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. NeuroImage, 125, 120-130. https://doi.org/10.1016/j.neuroimage.2015.10.042