A digital pediatric brain structure atlas from t1-weighted MR images

  • Authors:

  • Zuyao Y. Shan;Carlos Parra;Qing Ji;Robert J. Ogg;Yong Zhang;Fred H. Laningham;Wilburn E. Reddick

  • Affiliations:

  • Division of Translational Imaging Research, Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee;Department of Biomedical Engineering, The University of Memphis, Memphis, TN;Division of Translational Imaging Research, Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee;Division of Translational Imaging Research, Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee;Division of Translational Imaging Research, Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee;Division of Diagnostic Imaging, Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee;Division of Translational Imaging Research, Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee

  • Venue:
  • MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
  • Year:
  • 2006

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Abstract

Human brain atlases are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1-weighted MR data set of a 9-year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. We constructed a 3D triangular mesh model for each structure by triangulation of the structure’s reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/brainatlas) can be used to plan treatment, to conduct knowledge and model-driven segmentation, and to analyze the shapes of brain structures in pediatric patients.