  In this paper, we present a novel method for analysis and segmentation of
laminar structure of the cortex based on tissue characteristics whose change
across the gray matter underlies distinctive between cortical layers. We
develop and analyze features of individual neurons to investigate changes in
cytoarchitectonic differentiation and present a novel high-performance,
automated framework for neuron-level histological image analysis. Local tissue
and cell descriptors such as density, neuron size and other measures are used
for development of more complex neuron features used in machine learning model
trained on data manually labeled by three human experts. Final neuron layer
classifications were obtained by training a separate model for each expert and
combining their probability outputs. Importances of developed neuron features
on both global model level and individual prediction level are presented and
discussed.
