000911742 001__ 911742
000911742 005__ 20221123131044.0
000911742 0247_ $$2doi$$a10.25493/TKTP-7NR
000911742 037__ $$aFZJ-2022-04994
000911742 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$ufzj
000911742 245__ $$aUltrahigh resolution 3D cytoarchitectonic map of the human amygdala created by a Deep-Learning assisted workflow (v1)
000911742 260__ $$bEBRAINS$$c2022
000911742 3367_ $$2BibTeX$$aMISC
000911742 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1669201885_20076
000911742 3367_ $$026$$2EndNote$$aChart or Table
000911742 3367_ $$2DataCite$$aDataset
000911742 3367_ $$2ORCID$$aDATA_SET
000911742 3367_ $$2DINI$$aResearchData
000911742 520__ $$aThis dataset contains automatically created detailed map of 13 cytoarchitectonic subdivisions of the amygdala and 6 fiber bundles in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al 2021, which were trained on delineations on at least every 15th section created based on Kedo et al 2018. Mappings are available on every section. Their quality was observed by a trained neuroscientist to exclude sections with low quality results from further processing. Automatic mappings were transformed to the 3D reconstructed BigBrain space using transformations used in Amunts et al 2013, which were provided by Claude Lepage (McGill). Mappings on individual sections were used to assemble 3D volumes of all areas. Low quality results were replaced by interpolation between nearest neighboring sections. The volumes were then smoothed using a 3D median filter and largest connected components were identified to remove false positive results of the classification algorithm. The dataset consists of a HDF5 file containing the volume in RAS dimension ordering (20-micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset “interpolation_info” contains an integer vector with an integer value for each section which indicates if a section was replaced by interpolation due to low quality results (value 2) or not (value 1). Due to the large size of the volume, it is recommended to view the data online using the provided viewer link.
000911742 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000911742 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000911742 588__ $$aDataset connected to DataCite
000911742 650_7 $$2Other$$aNeuroscience
000911742 7001_ $$0P:(DE-Juel1)131650$$aKedo, O.$$b1$$ufzj
000911742 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
000911742 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000911742 773__ $$a10.25493/TKTP-7NR
000911742 909CO $$ooai:juser.fz-juelich.de:911742$$popenaire$$pVDB$$pec_fundedresources
000911742 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
000911742 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131650$$aForschungszentrum Jülich$$b1$$kFZJ
000911742 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b2$$kFZJ
000911742 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b3$$kFZJ
000911742 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000911742 9141_ $$y2022
000911742 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000911742 980__ $$adataset
000911742 980__ $$aVDB
000911742 980__ $$aI:(DE-Juel1)INM-1-20090406
000911742 980__ $$aUNRESTRICTED