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

arXiv: https://arxiv.org/abs/1705.10545 · HTML: https://mlsanity.com/paper/1705.10545/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2017.

## Built on by (most cited first, 4 of 4)

- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks](https://mlsanity.com/paper/2103.05259/) (2021)
- [From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging](https://mlsanity.com/paper/2505.11394/) (2025)
- [CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training](https://mlsanity.com/paper/2601.12282/) (2026)

## Abstract (arXiv)

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.