Rivulet

3D neuron morphology tracing with iterative back-tracking

Siqi Liu*, Donghao Zhang, Sidong Liu, Dagan Feng, Hanchuan Peng, Weidong Cai

*Corresponding author for this work

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative back-tracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises. By evaluating the proposed pipeline with the data provided by the Diadem challenge and the recent BigNeuron project, Rivulet is shown to be robust to challenging microscopic imagestacks. We discussed the algorithm design in technical details regarding the relationships between the proposed algorithm and the other state-of-the-art neuron tracing algorithms.

Original languageEnglish
Pages (from-to)387-401
Number of pages15
JournalNeuroinformatics
Volume14
Issue number4
DOIs
Publication statusPublished - 1 Oct 2016
Externally publishedYes

Keywords

  • 3D neuron reconstruction
  • Neuron morphology

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