3D Neuron Morphology Analysis

TL;DR

This paper introduces a novel 3D neuron morphology analysis method that uses skeleton mesh representations and unsupervised learning to classify complex neuron shapes, overcoming limitations of traditional curve skeletons.

Contribution

It proposes a new skeleton mesh concept and a method to compute it from 3D surface point clouds, enabling analysis of more complex neuron shapes.

Findings

Method is robust for analyzing neuron morphology.

Effective in extracting sub-cellular features.

Accurate neuron classification demonstrated.

Abstract

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.