Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI.
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.
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Dan
D
Wu
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ting
T
Ma
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Can
C
Ceritoglu
AnatomyWorks, LLC, Baltimore, MD, USA.
Yue
Y
Li
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jill
J
Chotiyanonta
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Zhipeng
Z
Hou
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
John
J
Hsu
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Xin
X
Xu
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Timothy
T
Brown
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Michael I
MI
Miller
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. Electronic address: susumu@mri.jhu.edu.
Susumu
S
Mori
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000716:Anatomy, Artistic; D001271:Atlases as Topic; D001921:Brain / Q000033:anatomy & histology*; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D055815:Young Adult
Atlas creation strategy; Dynamic age-matching; Hierarchical ontology; Multi-atlas; Segmentation accuracy; T1-weighted