, Volume 17, Issue 2, pp 185–196 | Cite as

FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree

  • Jian Yang
  • Ming Hao
  • Xiaoyang Liu
  • Zhijiang Wan
  • Ning ZhongEmail author
  • Hanchuan PengEmail author
Original Article


Neuron reconstruction is an important technique in computational neuroscience. Although there are many reconstruction algorithms, few can generate robust results. In this paper, we propose a reconstruction algorithm called fast marching spanning tree (FMST). FMST is based on a minimum spanning tree method (MST) and improve its performance in two aspects: faster implementation and no loss of small branches. The contributions of the proposed method are as follows. Firstly, the Euclidean distance weight of edges in MST is improved to be a more reasonable value, which is related to the probability of the existence of an edge. Secondly, a strategy of pruning nodes is presented, which is based on the radius of a node’s inscribed ball. Thirdly, separate branches of broken neuron reconstructions can be merged into a single tree. FMST and many other state of the art reconstruction methods were implemented on two datasets: 120 Drosophila neurons and 163 neurons with gold standard reconstructions. Qualitative and quantitative analysis on experimental results demonstrates that the performance of FMST is good compared with many existing methods. Especially, on the 91 fruitfly neurons with gold standard and evaluated by five metrics, FMST is one of two methods with best performance among all 27 state of the art reconstruction methods. FMST is a good and practicable neuron reconstruction algorithm, and can be implemented in Vaa3D platform as a neuron tracing plugin.


Neuron reconstruction Neuron morphology Minimum spanning tree Fast marching 



The authors thank the BigNeuron community for providing the data and the discussions, especially Dr. Zhi Zhou at Allen Institute for Brain Science. This work is partially supported by the National Basic Research Program of China (No. 2014CB744600), National Natural Science Foundation of China (No. 61420106005), and Beijing Natural Science Foundation (No. 4164080).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of MRI and Brain InformaticsBeijingChina
  3. 3.Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijingChina
  4. 4.Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijingChina
  5. 5.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan
  6. 6.Allen Institute for Brain ScienceSeattleUSA

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