Home > Publications database > Ultrahigh resolution 3D cytoarchitectonic map of the LGB (lam 1-6, CGL, Metathalamus) created by a Deep-Learning assisted workflow > print |
001 | 903075 | ||
005 | 20211209142054.0 | ||
024 | 7 | _ | |a 10.25493/33Z0-BX |2 doi |
037 | _ | _ | |a FZJ-2021-04804 |
100 | 1 | _ | |a Schiffer, C. |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |u fzj |
245 | _ | _ | |a Ultrahigh resolution 3D cytoarchitectonic map of the LGB (lam 1-6, CGL, Metathalamus) created by a Deep-Learning assisted workflow |
260 | _ | _ | |c 2021 |
336 | 7 | _ | |a MISC |2 BibTeX |
336 | 7 | _ | |a Dataset |b dataset |m dataset |0 PUB:(DE-HGF)32 |s 1639050106_4180 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a Chart or Table |0 26 |2 EndNote |
336 | 7 | _ | |a Dataset |2 DataCite |
336 | 7 | _ | |a DATA_SET |2 ORCID |
336 | 7 | _ | |a ResearchData |2 DINI |
520 | _ | _ | |a This 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). |
536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
588 | _ | _ | |a Dataset connected to DataCite |
650 | _ | 7 | |a Neuroscience |2 Other |
700 | 1 | _ | |a Brandstetter, A. |0 P:(DE-Juel1)169263 |b 1 |u fzj |
700 | 1 | _ | |a Bolakhrif, N. |0 P:(DE-Juel1)180739 |b 2 |u fzj |
700 | 1 | _ | |a Mohlberg, H. |0 P:(DE-Juel1)131660 |b 3 |u fzj |
700 | 1 | _ | |a Amunts, K. |0 P:(DE-Juel1)131631 |b 4 |u fzj |
700 | 1 | _ | |a Dickscheid, T. |0 P:(DE-Juel1)165746 |b 5 |u fzj |
773 | _ | _ | |a 10.25493/33Z0-BX |
909 | C | O | |o oai:juser.fz-juelich.de:903075 |p openaire |p VDB |p ec_fundedresources |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)170068 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)169263 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)180739 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)131660 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)131631 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)165746 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
914 | 1 | _ | |y 2021 |
920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
980 | _ | _ | |a dataset |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
980 | _ | _ | |a UNRESTRICTED |
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