Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain
This work addresses the challenge of efficient and accurate neuron counting in brain research, which is crucial for neuroscientists studying brain structure and diseases, though it is incremental as it builds on existing anisotropic diffusion models with novel training approaches.
The paper tackles the problem of automatically detecting neurons in NeuN-stained histological images of the human brain cortex, achieving over 95% accuracy in distinguishing neuron bodies on test data with faster processing times than comparable methods.
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.