Towards computational analytics of 3D neuron images using deep adversarial learning

Li, Zhongyu; Fan, Xiayue; Shang, Zengyi; Zhang, Lina; Zhen, Haotian; Fang, Chaowei

doi:10.1016/j.neucom.2020.03.129

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…To solve the problem of handcrafted feature, several learning based methods [ 14 – 16 ] have been proposed recently to classify neuron skeletons. References [ 14 , 15 ] project 3D skeletons back to 2D images and use convolution neural networks to get skeleton representation from 2D images. However, 3D shape information is lost when projecting onto 2D images even with multi-view projections like [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A robust approach to 3D neuron shape representation for quantification and classification

Jiang,

Goebel,

Borba

et al. 2023

BMC Bioinformatics

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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.

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