000903075 001__ 903075
000903075 005__ 20211209142054.0
000903075 0247_ $$2doi$$a10.25493/33Z0-BX
000903075 037__ $$aFZJ-2021-04804
000903075 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, C.$$b0$$eCorresponding author$$ufzj
000903075 245__ $$aUltrahigh resolution 3D cytoarchitectonic map of the LGB (lam 1-6, CGL, Metathalamus) created by a Deep-Learning assisted workflow
000903075 260__ $$c2021
000903075 3367_ $$2BibTeX$$aMISC
000903075 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1639050106_4180
000903075 3367_ $$026$$2EndNote$$aChart or Table
000903075 3367_ $$2DataCite$$aDataset
000903075 3367_ $$2ORCID$$aDATA_SET
000903075 3367_ $$2DINI$$aResearchData
000903075 520__ $$aThis dataset contains automatically created cytoarchitectonic maps of the six distinct layers (LGB-lam1-6) of the lateral geniculate body – LGB (CGL, Metathalamus) in the BigBrain (LGB is equivalent to CGL and can be used as synonyms). Mappings were created using Deep Convolutional Neural networks trained on delineations on every 30th section manually delineated on 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 5³ 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).
000903075 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000903075 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000903075 588__ $$aDataset connected to DataCite
000903075 650_7 $$2Other$$aNeuroscience
000903075 7001_ $$0P:(DE-Juel1)169263$$aBrandstetter, A.$$b1$$ufzj
000903075 7001_ $$0P:(DE-Juel1)180739$$aBolakhrif, N.$$b2$$ufzj
000903075 7001_ $$0P:(DE-Juel1)131660$$aMohlberg, H.$$b3$$ufzj
000903075 7001_ $$0P:(DE-Juel1)131631$$aAmunts, K.$$b4$$ufzj
000903075 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, T.$$b5$$ufzj
000903075 773__ $$a10.25493/33Z0-BX
000903075 909CO $$ooai:juser.fz-juelich.de:903075$$popenaire$$pVDB$$pec_fundedresources
000903075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
000903075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169263$$aForschungszentrum Jülich$$b1$$kFZJ
000903075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180739$$aForschungszentrum Jülich$$b2$$kFZJ
000903075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131660$$aForschungszentrum Jülich$$b3$$kFZJ
000903075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b4$$kFZJ
000903075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b5$$kFZJ
000903075 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)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000903075 9141_ $$y2021
000903075 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000903075 980__ $$adataset
000903075 980__ $$aVDB
000903075 980__ $$aI:(DE-Juel1)INM-1-20090406
000903075 980__ $$aUNRESTRICTED