The analysis of high-resolution microscopic scans of histological brain sections enables the identification of cytoarchitectonic areas, which are defined by the spatial organization of neuronal cells. Cutting the brain into histological sections is necessary to enable image acquisition at sufficient resolution. However, this cutting process leads to a loss of information about the 3D structure of the brain. Recovering this structure is important to analyze cytoarchitectonic patterns that are not recognizable in individual 2D images.
Artifacts introduced during histological processing make 3D whole brain reconstruction challenging. The Bigbrain dataset, created from 7404 sections at 20 micron resolution, is the highest resolution reconstruction of a human brain available today. However, its resolution is not sufficient for the identification of most cytoarchitectonic areas. This motivates ongoing research that focuses on 3D reconstructions at 1 micron resolution.
We investigate how the availability of 3D brain reconstructions at 1 micron resolution will affect automated cytoarchitecture classification based on recently proposed deep learning algorithms. We use a recently released dataset consisting of two volumes of interest (VOIs) with a size of 6x6x6 mm³ extracted from the primary and secondary visual cortex in the BigBrain dataset. We experimentally identify challenges regarding the automated analysis of 3D reconstructed microscopy data and evaluate solutions for the identified challenges.