Precise segmentation of densely interweaving neuron clusters using G-Cut. Nat Commun 2019 Apr 04;10(1):1549
Date
04/06/2019Pubmed ID
30948706Pubmed Central ID
PMC6449501DOI
10.1038/s41467-019-09515-0Scopus ID
2-s2.0-85063996703 (requires institutional sign-in at Scopus site) 32 CitationsAbstract
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.
Author List
Li R, Zhu M, Li J, Bienkowski MS, Foster NN, Xu H, Ard T, Bowman I, Zhou C, Veldman MB, Yang XW, Hintiryan H, Zhang J, Dong HWAuthor
Matthew B. Veldman PhD Assistant Professor in the Cell Biology, Neurobiology and Anatomy department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsBrain Mapping
Computational Biology
Computer Simulation
Models, Theoretical
Nerve Net
Neurons
