DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox

Min Liu; Zhuangdian Lin; Weixun Chen; Erik Meijering; Yaonan Wang

doi:10.1007/978-1-0716-3969-6_12

DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox

Methods Mol Biol. 2024:2831:179-197. doi: 10.1007/978-1-0716-3969-6_12.

Authors

Min Liu^{1

2}, Zhuangdian Lin^{3

4}, Weixun Chen^{3

4}, Erik Meijering⁵, Yaonan Wang^{3

4}

Affiliations

¹ College of Electrical and Information Engineering, Hunan University, Hunan, China. [email protected].
² National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha, China. [email protected].
³ College of Electrical and Information Engineering, Hunan University, Hunan, China.
⁴ National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha, China.
⁵ School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.

PMID: 39134850
DOI: 10.1007/978-1-0716-3969-6_12

Abstract

Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. However, these ways are labor-intensive, especially when handling the huge volume of whole brain microscopy imaging data. Here, we present a deep-learning-based neuron morphology analysis toolbox (DNeuroMAT) for automated analysis of neuron microscopy images, which consists of three modules: neuron segmentation, neuron reconstruction, and neuron critical points detection.

Keywords: 3D neuron reconstruction; Critical points detection; Deep learning; Image analysis; Image segmentationImage segmentation.

MeSH terms

Animals
Brain / cytology
Deep Learning*
Humans
Image Processing, Computer-Assisted / methods
Imaging, Three-Dimensional* / methods
Neurons* / cytology
Software