# Computational analysis of laminar structure of the human cortex based on local neuron features

## TL;DR

This paper introduces a new automated framework for analyzing and segmenting the laminar structure of the human cortex using neuron-level features and machine learning, enhancing understanding of cortical organization.

## Contribution

It presents a novel method combining neuron features and machine learning for automated cortical layer segmentation, improving accuracy and interpretability.

## Key findings

- Neuron features significantly improve layer classification accuracy
- The framework achieves high performance on histological data
- Expert-specific models enhance classification reliability

## Abstract

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.