%0 Generic %A Avants, Brian %A Tustison, Nick %D 2014 %T ANTs/ANTsR Brain Templates %U https://figshare.com/articles/dataset/ANTs_ANTsR_Brain_Templates/915436 %R 10.6084/m9.figshare.915436.v1 %2 https://ndownloader.figshare.com/files/3133820 %2 https://ndownloader.figshare.com/files/3133826 %2 https://ndownloader.figshare.com/files/3133832 %2 https://ndownloader.figshare.com/files/3133838 %2 https://ndownloader.figshare.com/files/3133847 %K brain %K antsr %K registration %K segmentation %K Bioinformatics %K Neuroscience %X

Population level templates encode knowledge about the expected shape and appearance of brain structures within a given demographic cross-section. Spatial/neuroanatomical priors also encode specific knowledge about the identity and spatial relationships between structures in the brain. The prior knowledge encoded by brain templates can be propagated to new subjects in order to aid both segmentation and registration in large studies. This critical step allows us to automatically convert images into parseable information thus enabling images to be more easily combined with other information such as genomics, psychometrics or demographics, all crucial to bridging gaps in the era of big data.

These templates are intended for use with ANTs http://stnava.github.io/ANTs/  and ANTsR http://stnava.github.io/ANTsR/ medical image processing architectures.

We provide templates for 4 public neuroimaging datasets: IXI, Oasis, NKI-1 and Kirby/MMRR. Each template contains an average T1 neuroimage of the head and tissue priors for cortex, white matter, cerebrospinal fluid, deep gray matter, brainstem and the cerebellum.

Template information

IXI

COMPLETE_FEMALE_AGE_20_30
COMPLETE_FEMALE_AGE_30_40
COMPLETE_FEMALE_AGE_40_50
COMPLETE_FEMALE_AGE_50_60
COMPLETE_FEMALE_AGE_60_70
COMPLETE_FEMALE_AGE_70_80
COMPLETE_FEMALE_AGE_80_90
COMPLETE_MALE_AGE_20_30
COMPLETE_MALE_AGE_30_40
COMPLETE_MALE_AGE_40_50
COMPLETE_MALE_AGE_50_60
COMPLETE_MALE_AGE_60_70
COMPLETE_MALE_AGE_70_80
COMPLETE_MALE_AGE_80_90

MMRR
ID,AGE,M/F
KKI2009-01,25,M
KKI2009-02,61,F
KKI2009-03,20,F
KKI2009-04,25,M
KKI2009-05,25,M
KKI2009-06,28,M
KKI2009-07,30,M
KKI2009-08,49,F
KKI2009-09,26,M
KKI2009-10,38,F
KKI2009-11,25,M
KKI2009-12,26,F
KKI2009-13,30,M
KKI2009-14,38,M
KKI2009-15,34,M
KKI2009-16,42,F
KKI2009-17,38,M
KKI2009-18,26,M
KKI2009-19,26,F
KKI2009-20,28,M
KKI2009-21,38,F
KKI2009-22,30,F
KKI2009-23,29,F
KKI2009-24,30,M
KKI2009-25,25,M
KKI2009-26,34,M
KKI2009-27,29,F
KKI2009-28,32,M
KKI2009-29,49,F
KKI2009-30,28,F
KKI2009-31,25,M
KKI2009-32,23,F
KKI2009-33,28,F
KKI2009-34,30,M
KKI2009-35,42,F
KKI2009-36,23,F
KKI2009-37,61,F
KKI2009-38,26,M
KKI2009-39,22,F
KKI2009-40,32,M
KKI2009-41,22,F
KKI2009-42,26,M

NKI <= 10:

ID,AGE,M/F
1034049,9,M
1601547,8,F
1875434,8,F
2674565,4,M
2678751,10,F
2915821,10,F
2970212,10,F
3374719,7,M
3566919,9,M
3848143,9,M
3989122,6,F
5844518,5,M
9421819,10,F

NKI:  Random selection from all datasets ... likely average age ~ 35 - 40 with a large standard deviation.

OASIS:
Training
OAS1_0061,20,F
OAS1_0080,25,F
OAS1_0092,22,M
OAS1_0145,34,M
OAS1_0150,20,F
OAS1_0156,20,F
OAS1_0191,21,F
OAS1_0202,23,F
OAS1_0230,19,F
OAS1_0236,20,F
OAS1_0239,29,F
OAS1_0249,28,F
OAS1_0285,20,M  
OAS1_0353,22,M
OAS1_0368,22,M

Testing
OAS1_0101,29,M,1st scan
OAS1_0111,23,M,1st scan
OAS1_0117,25,M,1st scan
OAS1_0379,20,F,1st scan
OAS1_0395,26,F,1st scan
OAS1_0101,29,M,2nd scan
OAS1_0111,23,M,2nd scan
OAS1_0117,25,M,2nd scan
OAS1_0379,20,M,2nd scan
OAS1_0395,26,M,2nd scan
OAS1_0091,18,F
OAS1_0417,30,F
OAS1_0040,38,F
OAS1_0282,45,F
OAS1_0331,54,F
OAS1_0456,61,M
OAS1_0300,68,M
OAS1_0220,75,F
OAS1_0113,83,F
OAS1_0083,90,F

 

%I figshare