  Human brain atlases provide spatial reference systems for data characterizing
brain organization at different levels, coming from different brains.
Cytoarchitecture is a basic principle of the microstructural organization of
the brain, as regional differences in the arrangement and composition of
neuronal cells are indicators of changes in connectivity and function.
Automated scanning procedures and observer-independent methods are
prerequisites to reliably identify cytoarchitectonic areas, and to achieve
reproducible models of brain segregation. Time becomes a key factor when moving
from the analysis of single regions of interest towards high-throughput
scanning of large series of whole-brain sections. Here we present a new
workflow for mapping cytoarchitectonic areas in large series of cell-body
stained histological sections of human postmortem brains. It is based on a Deep
Convolutional Neural Network (CNN), which is trained on a pair of section
images with annotations, with a large number of un-annotated sections in
between. The model learns to create all missing annotations in between with
high accuracy, and faster than our previous workflow based on
observer-independent mapping. The new workflow does not require preceding
3D-reconstruction of sections, and is robust against histological artefacts. It
processes large data sets with sizes in the order of multiple Terabytes
efficiently. The workflow was integrated into a web interface, to allow access
without expertise in deep learning and batch computing. Applying deep neural
networks for cytoarchitectonic mapping opens new perspectives to enable
high-resolution models of brain areas, introducing CNNs to identify borders of
brain areas.
