Automated 3-D neuron tracing with precise branch erasing and confidence controlled back tracking

Siqi Liu, Donghao Zhang, Yang Song, Hanchuan Peng, Weidong Cai

Research output: Contribution to journalArticleResearchpeer-review

39 Citations (Scopus)


The automatic reconstruction of single neurons from microscopic images is essential to enable large-scale data-driven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3-D microscopic images without human intervention. In this paper, we developed a new algorithm for automatic 3-D neuron reconstruction. The main idea of the proposed algorithm is to iteratively track backward from the potential neuronal termini to the soma centre. An online confidence score is computed to decide if a tracing iteration should be stopped and discarded from the final reconstruction. The performance improvements comparing with the previous methods are mainly introduced by a more accurate estimation of the traced area and the confidence controlled back-tracking algorithm. The proposed algorithm supports large-scale batch-processing by requiring only one user specified parameter for background segmentation. We bench tested the proposed algorithm on the images obtained from both the DIADEM challenge and the BigNeuron challenge. Our proposed algorithm achieved the state-of-the-art results. 

Original languageEnglish
Article number8354803
Pages (from-to)2441-2452
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number11
Publication statusPublished - Nov 2018
Externally publishedYes


  • 3-D neuron reconstruction
  • neuron morphology

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