Precise segmentation of densely interweaving neuron clusters using G-Cut. View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Rui Li, Muye Zhu, Junning Li, Michael S Bienkowski, Nicholas N Foster, Hanpeng Xu, Tyler Ard, Ian Bowman, Changle Zhou, Matthew B Veldman, X William Yang, Houri Hintiryan, Junsong Zhang, Hong-Wei Dong

ABSTRACT

Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects. More... »

PAGES

1549

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-019-09515-0

DOI

http://dx.doi.org/10.1038/s41467-019-09515-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113184952

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30948706


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