TY - DATA T1 - ANTs/ANTsR Brain Templates PY - 2014/01/28 AU - Brian Avants AU - Nick Tustison UR - https://figshare.com/articles/dataset/ANTs_ANTsR_Brain_Templates/915436 DO - 10.6084/m9.figshare.915436.v1 L4 - https://ndownloader.figshare.com/files/3133820 L4 - https://ndownloader.figshare.com/files/3133826 L4 - https://ndownloader.figshare.com/files/3133832 L4 - https://ndownloader.figshare.com/files/3133838 L4 - https://ndownloader.figshare.com/files/3133847 KW - brain KW - antsr KW - registration KW - segmentation KW - Bioinformatics KW - Neuroscience N2 - 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_30COMPLETE_FEMALE_AGE_30_40COMPLETE_FEMALE_AGE_40_50COMPLETE_FEMALE_AGE_50_60COMPLETE_FEMALE_AGE_60_70COMPLETE_FEMALE_AGE_70_80COMPLETE_FEMALE_AGE_80_90COMPLETE_MALE_AGE_20_30COMPLETE_MALE_AGE_30_40COMPLETE_MALE_AGE_40_50COMPLETE_MALE_AGE_50_60COMPLETE_MALE_AGE_60_70COMPLETE_MALE_AGE_70_80COMPLETE_MALE_AGE_80_90 MMRRID,AGE,M/FKKI2009-01,25,MKKI2009-02,61,FKKI2009-03,20,FKKI2009-04,25,MKKI2009-05,25,MKKI2009-06,28,MKKI2009-07,30,MKKI2009-08,49,FKKI2009-09,26,MKKI2009-10,38,FKKI2009-11,25,MKKI2009-12,26,FKKI2009-13,30,MKKI2009-14,38,MKKI2009-15,34,MKKI2009-16,42,FKKI2009-17,38,MKKI2009-18,26,MKKI2009-19,26,FKKI2009-20,28,MKKI2009-21,38,FKKI2009-22,30,FKKI2009-23,29,FKKI2009-24,30,MKKI2009-25,25,MKKI2009-26,34,MKKI2009-27,29,FKKI2009-28,32,MKKI2009-29,49,FKKI2009-30,28,FKKI2009-31,25,MKKI2009-32,23,FKKI2009-33,28,FKKI2009-34,30,MKKI2009-35,42,FKKI2009-36,23,FKKI2009-37,61,FKKI2009-38,26,MKKI2009-39,22,FKKI2009-40,32,MKKI2009-41,22,FKKI2009-42,26,M NKI <= 10: ID,AGE,M/F1034049,9,M1601547,8,F1875434,8,F2674565,4,M2678751,10,F2915821,10,F2970212,10,F3374719,7,M3566919,9,M3848143,9,M3989122,6,F5844518,5,M9421819,10,F NKI:  Random selection from all datasets ... likely average age ~ 35 - 40 with a large standard deviation. OASIS:TrainingOAS1_0061,20,FOAS1_0080,25,FOAS1_0092,22,MOAS1_0145,34,MOAS1_0150,20,FOAS1_0156,20,FOAS1_0191,21,FOAS1_0202,23,FOAS1_0230,19,FOAS1_0236,20,FOAS1_0239,29,FOAS1_0249,28,FOAS1_0285,20,M  OAS1_0353,22,MOAS1_0368,22,M TestingOAS1_0101,29,M,1st scanOAS1_0111,23,M,1st scanOAS1_0117,25,M,1st scanOAS1_0379,20,F,1st scanOAS1_0395,26,F,1st scanOAS1_0101,29,M,2nd scanOAS1_0111,23,M,2nd scanOAS1_0117,25,M,2nd scanOAS1_0379,20,M,2nd scanOAS1_0395,26,M,2nd scanOAS1_0091,18,FOAS1_0417,30,FOAS1_0040,38,FOAS1_0282,45,FOAS1_0331,54,FOAS1_0456,61,MOAS1_0300,68,MOAS1_0220,75,FOAS1_0113,83,FOAS1_0083,90,F   ER -