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| Home > Publications database > Ultrahigh resolution 3D cytoarchitectonic map of the LGB (lam 1-6, CGL, Metathalamus) created by a Deep-Learning assisted workflow |
| Home > Publications database > Ultrahigh resolution 3D cytoarchitectonic map of the LGB (lam 1-6, CGL, Metathalamus) created by a Deep-Learning assisted workflow |
| Dataset | FZJ-2021-04804 |
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2021
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Please use a persistent id in citations: doi:10.25493/33Z0-BX
Abstract: 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).
Keyword(s): Neuroscience
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