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| Hauptseite > Publikationsdatenbank > Ultrahigh resolution 3D cytoarchitectonic map of Area hOc5 (LOC) created by a Deep-Learning assisted workflow > print |
| Hauptseite > Publikationsdatenbank > Ultrahigh resolution 3D cytoarchitectonic map of Area hOc5 (LOC) created by a Deep-Learning assisted workflow > print |
| 001 | 888519 | ||
| 005 | 20210130011002.0 | ||
| 024 | 7 | _ | |a 10.25493/2V62-TTG |2 doi |
| 037 | _ | _ | |a FZJ-2020-04983 |
| 100 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Ultrahigh resolution 3D cytoarchitectonic map of Area hOc5 (LOC) created by a Deep-Learning assisted workflow |
| 260 | _ | _ | |c 2020 |b EBRAINS |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Dataset |b dataset |m dataset |0 PUB:(DE-HGF)32 |s 1607355264_32080 |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 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). |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |x 2 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a Kiwitz, Kai |0 P:(DE-Juel1)171890 |b 1 |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 2 |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 3 |
| 773 | _ | _ | |a 10.25493/2V62-TTG |
| 909 | C | O | |o oai:juser.fz-juelich.de:888519 |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)171890 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)131631 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)165746 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2020 |
| 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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