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

TitleA robust approach to 3D neuron shape representation for quantification and classification.
Publication TypeJournal Article
Year of Publication2023
AuthorsJiang J, Goebel M, Borba C, Smith W, Manjunath BS
JournalBMC Bioinformatics
Volume24
Issue1
Pagination366
Date Published2023 Sep 28
ISSN1471-2105
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.

DOI10.1186/s12859-023-05482-y
Alternate JournalBMC Bioinformatics
PubMed ID37770830
PubMed Central IDPMC10537603
Grant List5R01NS103774-04 / NH / NIH HHS / United States