guest ::
| Home > Publications database > Going 3D with AI: Full 3D Reconstructions of Cytoarchitectonic Maps in BigBrain > print |
| Home > Publications database > Going 3D with AI: Full 3D Reconstructions of Cytoarchitectonic Maps in BigBrain > print |
| 001 | 1048760 | ||
| 005 | 20251202203137.0 | ||
| 037 | _ | _ | |a FZJ-2025-04875 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Lothmann, Kimberley |0 P:(DE-Juel1)196766 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a EBRAINS summit 2025 |c Brüssel |d 2025-12-08 - 2025-12-11 |w Belgien |
| 245 | _ | _ | |a Going 3D with AI: Full 3D Reconstructions of Cytoarchitectonic Maps in BigBrain |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1764669534_20668 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Introduction: As part of the Julich-Brain Atlas (Amunts et al., 2020), the BigBrain dataset (Amunts et al., 2013) provides the first ultrahigh-resolution 3D model of the human brain at 20 μm isotropic resolution, reconstructed from 7,404 histological sections. It enables cytoarchitectonic mapping at a level of detail that bridges microscopic organization with macroscale imaging. Traditionally, areas have been delineated on 2D sections, limiting their integration into 3D brain reference spaces. Methods: We developed a hybrid workflow combining expert identification of cortical areas with deep learningbased 3D reconstruction. Using the ATLaSUI interface (Schiffer et al., 2021), neuroscientists annotated every 10th – 15th histological section, providing training data for the CytoNet model (manuscript in preparation). CytoNet infers cortical layer continuity and areal boundaries between annotated sections, avoiding geometric interpolation and preserving cytoarchitectonic detail. Largescale model inference and reconstruction were performed on the JURECA-DC supercomputer (Jülich Supercomputing Centre). Results: Currently, 33 cortical BigBrain areas are publicly available through the siibra tool suite and EBRAINS Knowledge Graph. An additional 56 cortical areas, including eight newly mapped regions, were reconstructed at 20 μm resolution, expanding the Julich-Brain Atlas to a total of 98 BigBrain areas. All maps will be openly accessible via EBRAINS, enabling interactive and programmatic exploration within a unified reference framework. Discussion: Since areas were reconstructed independently, minor overlaps can occur at region borders, especially in highly folded cortical zones. Sampling every 10th – 15th section may also introduce interpolation artifacts in regions with steep cytoarchitectonic transitions. Future work will focus on multi-area optimization to reduce boundary inconsistencies and improve 3D continuity. These ongoing developments advance the integration of microstructural and macroscale brain data within a coherent human brain reference space. Keywords: BigBrain, Julich Brain Atlas, Cytoarchitecture, 3D Reconstruction, Deep Learning, Human Brain Atlas, EBRAINS References Amunts K, Lepage C, Borgeat L, et al. BigBrain: An ultrahigh-resolution 3D human brain model. Science. 2013;340(6139):1472-1475. doi:10.1126/science.1235381 Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science. 2020;369(6506):988-992. doi:10.1126/science.abb4588 Schiffer C, Spitzer H, Kiwitz K, et al. Convolutional neural networks for cytoarchitectonic brain mapping at large scale. Neuroimage. 2021;240:118327. doi:10.1016/j.neuroimage.2021.118327 Acknowledgments: This project received funding from the European Union’s Horizon 2020 Research and InnovationProgramme, grant agreement 101147319 (EBRAINS 2.0 Project), the Helmholtz Association portfolio theme “Supercomputing and Modeling for the Human Brain”, the Helmholtz Association’sInitiative and Networking Fund through the Helmholtz International BigBrain Analytics and LearningLaboratory (HIBALL) under the Helmholtz International Lab grant agreement InterLabs-0015, fromHELMHOLTZ IMAGING, a platform of the Helmholtz Information \& Data Science Incubator [XBRAIN, grant number: ZT-I-PF-4-061], and from the Deutsche Forschungsgemeinschaft (DFG,German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 –501864659. |
| 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 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 1 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 2 |
| 536 | _ | _ | |a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015) |0 G:(DE-HGF)InterLabs-0015 |c InterLabs-0015 |x 3 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 4 |
| 536 | _ | _ | |a X-BRAIN (ZT-I-PF-4-061) |0 G:(DE-HGF)ZT-I-PF-4-061 |c ZT-I-PF-4-061 |x 5 |
| 536 | _ | _ | |a DFG project G:(GEPRIS)501864659 - NFDI4BIOIMAGE - Nationale Forschungsdateninfrastruktur für Mikroskopie und Bildanalyse (501864659) |0 G:(GEPRIS)501864659 |c 501864659 |x 6 |
| 700 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 1 |u fzj |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 2 |u fzj |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 3 |u fzj |
| 856 | 4 | _ | |u https://cdn.prod.website-files.com/67fd5fb0e5cec89dfb6b65be/692d689da7f10aeb4b0bded3_EBRAINS-Summit-2025-Book-of-Abstracts-1-Dec.pdf |
| 909 | C | O | |o oai:juser.fz-juelich.de:1048760 |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)196766 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)170068 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)165746 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)131631 |
| 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 |
| 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)POF4-5251 |x 1 |
| 914 | 1 | _ | |y 2025 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|