This title appears in the Scientific Report :
2022
Please use the identifier:
http://dx.doi.org/10.25493/ZR6F-V8P in citations.
Ultrahigh-resolution 3D cytoarchitectonic map of Area MFG2 of the human anterior dorsolateral prefrontal cortex (DLPFC) created by a Deep-Learning assisted workflow (v1)
Ultrahigh-resolution 3D cytoarchitectonic map of Area MFG2 of the human anterior dorsolateral prefrontal cortex (DLPFC) created by a Deep-Learning assisted workflow (v1)
This dataset contains automatically created detailed map of the area MFG2 of the human anterior dorsolateral prefrontal cortex (DLPFC) in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al. 2021, which were trained on delineations on at l...
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Personal Name(s): | Schiffer, Christian |
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Bruno, Ariane / Amunts, Katrin / Dickscheid, Timo | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 |
Imprint: |
EBRAINS
2022
|
DOI: |
10.25493/ZR6F-V8P |
Document Type: |
Dataset |
Research Program: |
Human Brain Project Specific Grant Agreement 3 Neuroscientific Data Analytics and AI |
Subject (ZB): | |
Publikationsportal JuSER |
This dataset contains automatically created detailed map of the area MFG2 of the human anterior dorsolateral prefrontal cortex (DLPFC) in the BigBrain dataset. The mappings were created using Deep Convolutional Neural Networks based on Schiffer et al. 2021, which were trained on delineations on at least every 30th section created based on Bruno et al. 2022. Mappings are available on every section. Their quality was observed by a trained neuroscientist to exclude sections with low-quality results from further processing. Automatic mappings were transformed to the 3D reconstructed BigBrain space using transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill). Mappings on individual sections were used to assemble 3D volumes of all areas. Low-quality results were replaced by interpolation between nearest neighboring sections. The volumes were then smoothed using a 3D median filter, and the largest connected components were identified to remove false positive results of the classification algorithm. The dataset consists of an 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 with an integer value for each section which indicates if a section was replaced by interpolation due to low-quality results (value 2) or not (value 1). Due to the large size of the volume, it is recommended to view the data online using the provided viewer link. |