Precise segmentation of densely interweaving neuron clusters using G-Cut

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 () and Hong-Wei Dong ()
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Rui Li: Xiamen University
Muye Zhu: University of Southern California
Junning Li: University of Southern California
Michael S. Bienkowski: University of Southern California
Nicholas N. Foster: University of Southern California
Hanpeng Xu: University of Southern California
Tyler Ard: University of Southern California
Ian Bowman: University of Southern California
Changle Zhou: Xiamen University
Matthew B. Veldman: University of California at Los Angeles
X. William Yang: University of California at Los Angeles
Houri Hintiryan: University of Southern California
Junsong Zhang: Xiamen University
Hong-Wei Dong: University of Southern California

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-09515-0

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