000888519 001__ 888519
000888519 005__ 20210130011002.0
000888519 0247_ $$2doi$$a10.25493/2V62-TTG
000888519 037__ $$aFZJ-2020-04983
000888519 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$eCorresponding author
000888519 245__ $$aUltrahigh resolution 3D cytoarchitectonic map of Area hOc5 (LOC) created by a Deep-Learning assisted workflow
000888519 260__ $$bEBRAINS$$c2020
000888519 3367_ $$2BibTeX$$aMISC
000888519 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1607355264_32080
000888519 3367_ $$026$$2EndNote$$aChart or Table
000888519 3367_ $$2DataCite$$aDataset
000888519 3367_ $$2ORCID$$aDATA_SET
000888519 3367_ $$2DINI$$aResearchData
000888519 520__ $$aThis dataset contains automatically created cytoarchitectonic maps of Area hOc5 (LOC) in the BigBrain. Mappings were created using Deep Convolutional Neural networks trained on delineations on every 60th section using multivariate statistical image analysis, applied to GLI-images of coronal histological sections of 1 micron resolution. Resulting mappings are available on every section. Maps were transformed to the 3D reconstructed BigBrain space. Individual sections were used to assemble a 3D volume of the area, low quality results were replaced by interpolations between nearest neighboring sections. The volume was then smoothed using an 11³ median filter and largest connected components were identified to remove false positive results. 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 for each section which indicates if a section was interpolated due to low quality results (value 2) or not (value 1).
000888519 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000888519 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000888519 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x2
000888519 588__ $$aDataset connected to DataCite
000888519 7001_ $$0P:(DE-Juel1)171890$$aKiwitz, Kai$$b1
000888519 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2
000888519 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3
000888519 773__ $$a10.25493/2V62-TTG
000888519 909CO $$ooai:juser.fz-juelich.de:888519$$pec_fundedresources$$pVDB$$popenaire
000888519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
000888519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171890$$aForschungszentrum Jülich$$b1$$kFZJ
000888519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b2$$kFZJ
000888519 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b3$$kFZJ
000888519 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000888519 9141_ $$y2020
000888519 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000888519 980__ $$adataset
000888519 980__ $$aVDB
000888519 980__ $$aI:(DE-Juel1)INM-1-20090406
000888519 980__ $$aUNRESTRICTED