Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain

TL;DR

This paper introduces a novel anisotropic diffusion model for automatic neuron detection in high-resolution human brain histological images, addressing variability among experts and achieving high accuracy and efficiency.

Contribution

It presents a new PDE-based approach for neuron detection that accounts for expert variability and improves detection accuracy and speed.

Findings

Correctly identified over 95% of neurons in test data.

Reduced detection time compared to existing methods.

Addresses expert variability in neuron annotation.

Abstract

In this paper, we present a novel use of an anisotropic diffusion model for automatic detection of neurons in histological sections of the adult human brain cortex. We use a partial differential equation model to process high resolution images to acquire locations of neuronal bodies. We also present a novel approach in model training and evaluation that considers variability among the human experts, addressing the issue of existence and correctness of the golden standard for neuron and cell counting, used in most of relevant papers. Our method, trained on dataset manually labeled by three experts, has correctly distinguished over 95% of neuron bodies in test data, doing so in time much shorter than other comparable methods.