Automated Neuron Shape Analysis from Electron Microscopy

TL;DR

This paper introduces an automated framework for analyzing neuron shapes from high-resolution electron microscopy data, enabling detailed morphological studies at a large scale that were previously impractical.

Contribution

It presents a novel automated method combining shape extraction, autoencoder-based representation, and shape distribution analysis for neuron classification from EM datasets.

Findings

Effective clustering of neuronal shapes achieved

Automated analysis outperforms manual methods

Framework applied to over 1000 neurons

Abstract

Morphology based analysis of cell types has been an area of great interest to the neuroscience community for several decades. Recently, high resolution electron microscopy (EM) datasets of the mouse brain have opened up opportunities for data analysis at a level of detail that was previously impossible. These datasets are very large in nature and thus, manual analysis is not a practical solution. Of particular interest are details to the level of post synaptic structures. This paper proposes a fully automated framework for analysis of post-synaptic structure based neuron analysis from EM data. The processing framework involves shape extraction, representation with an autoencoder, and whole cell modeling and analysis based on shape distributions. We apply our novel framework on a dataset of 1031 neurons obtained from imaging a 1mm x 1mm x 40 micrometer volume of the mouse visual cortex and show the strength of our method in clustering and classification of neuronal shapes.