ANTs/ANTsR Brain Templates

2014-01-28T20:31:01Z (GMT) by Brian Avants Nick Tustison
<p>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.</p> <p>These templates are intended for use with ANTs http://stnava.github.io/ANTs/  and ANTsR http://stnava.github.io/ANTsR/ medical image processing architectures.</p> <p>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.</p> <p>Template information</p> <p>IXI</p> <p>COMPLETE_FEMALE_AGE_20_30<br>COMPLETE_FEMALE_AGE_30_40<br>COMPLETE_FEMALE_AGE_40_50<br>COMPLETE_FEMALE_AGE_50_60<br>COMPLETE_FEMALE_AGE_60_70<br>COMPLETE_FEMALE_AGE_70_80<br>COMPLETE_FEMALE_AGE_80_90<br>COMPLETE_MALE_AGE_20_30<br>COMPLETE_MALE_AGE_30_40<br>COMPLETE_MALE_AGE_40_50<br>COMPLETE_MALE_AGE_50_60<br>COMPLETE_MALE_AGE_60_70<br>COMPLETE_MALE_AGE_70_80<br>COMPLETE_MALE_AGE_80_90</p> <p>MMRR<br>ID,AGE,M/F<br>KKI2009-01,25,M<br>KKI2009-02,61,F<br>KKI2009-03,20,F<br>KKI2009-04,25,M<br>KKI2009-05,25,M<br>KKI2009-06,28,M<br>KKI2009-07,30,M<br>KKI2009-08,49,F<br>KKI2009-09,26,M<br>KKI2009-10,38,F<br>KKI2009-11,25,M<br>KKI2009-12,26,F<br>KKI2009-13,30,M<br>KKI2009-14,38,M<br>KKI2009-15,34,M<br>KKI2009-16,42,F<br>KKI2009-17,38,M<br>KKI2009-18,26,M<br>KKI2009-19,26,F<br>KKI2009-20,28,M<br>KKI2009-21,38,F<br>KKI2009-22,30,F<br>KKI2009-23,29,F<br>KKI2009-24,30,M<br>KKI2009-25,25,M<br>KKI2009-26,34,M<br>KKI2009-27,29,F<br>KKI2009-28,32,M<br>KKI2009-29,49,F<br>KKI2009-30,28,F<br>KKI2009-31,25,M<br>KKI2009-32,23,F<br>KKI2009-33,28,F<br>KKI2009-34,30,M<br>KKI2009-35,42,F<br>KKI2009-36,23,F<br>KKI2009-37,61,F<br>KKI2009-38,26,M<br>KKI2009-39,22,F<br>KKI2009-40,32,M<br>KKI2009-41,22,F<br>KKI2009-42,26,M</p> <p>NKI <= 10:</p> <p>ID,AGE,M/F<br>1034049,9,M<br>1601547,8,F<br>1875434,8,F<br>2674565,4,M<br>2678751,10,F<br>2915821,10,F<br>2970212,10,F<br>3374719,7,M<br>3566919,9,M<br>3848143,9,M<br>3989122,6,F<br>5844518,5,M<br>9421819,10,F</p> <p>NKI:  Random selection from all datasets ... likely average age ~ 35 - 40 with a large standard deviation.</p> <p>OASIS:<br>Training<br>OAS1_0061,20,F<br>OAS1_0080,25,F<br>OAS1_0092,22,M<br>OAS1_0145,34,M<br>OAS1_0150,20,F<br>OAS1_0156,20,F<br>OAS1_0191,21,F<br>OAS1_0202,23,F<br>OAS1_0230,19,F<br>OAS1_0236,20,F<br>OAS1_0239,29,F<br>OAS1_0249,28,F<br>OAS1_0285,20,M  <br>OAS1_0353,22,M<br>OAS1_0368,22,M</p> <p>Testing<br>OAS1_0101,29,M,1st scan<br>OAS1_0111,23,M,1st scan<br>OAS1_0117,25,M,1st scan<br>OAS1_0379,20,F,1st scan<br>OAS1_0395,26,F,1st scan<br>OAS1_0101,29,M,2nd scan<br>OAS1_0111,23,M,2nd scan<br>OAS1_0117,25,M,2nd scan<br>OAS1_0379,20,M,2nd scan<br>OAS1_0395,26,M,2nd scan<br>OAS1_0091,18,F<br>OAS1_0417,30,F<br>OAS1_0040,38,F<br>OAS1_0282,45,F<br>OAS1_0331,54,F<br>OAS1_0456,61,M<br>OAS1_0300,68,M<br>OAS1_0220,75,F<br>OAS1_0113,83,F<br>OAS1_0083,90,F</p> <p> </p>