# Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks

## TL;DR

This paper introduces an automatic convolutional neural network-based method for high-resolution histological parcellation of the human visual cortex, improving efficiency and consistency over manual approaches.

## Contribution

The study presents a novel CNN model that combines topological and texture features for scalable, accurate brain region parcellation at microscopic resolution.

## Key findings

- Model achieves accurate parcellation of visual cortex areas.
- Predictions are transferable across different brains.
- Method ensures spatial consistency across sections.

## Abstract

Microscopic analysis of histological sections is considered the "gold standard" to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2um resolution. It is based on a convolutional neural network that combines topological information from probabilistic atlases with the texture features learned from high-resolution cell-body stained images. The model is applied to visual areas and trained on a sparse set of partial annotations. We show how predictions are transferable to new brains and spatially consistent across sections.