Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
This work addresses the problem of scaling high-resolution brain mapping for neuroscientists, representing an incremental improvement by automating a previously semiautomatic process.
The paper tackles the time-consuming and non-scalable process of cytoarchitectonic mapping in human brain histological sections by presenting an automatic parcellation approach using a convolutional neural network that combines probabilistic atlases with texture features, achieving spatially consistent predictions transferable to new brains at 2um resolution.
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.