Abstract
Neuron reconstruction is an important technique in computational neuroscience. There are many neuron reconstruction algorithms, but few can generate robust result, especially when a 3D microscopic image has low single-to-noise ratio. In this paper we propose a neuron reconstruction algorithm called fast marching spanning tree (FMST), which is based on minimum spanning tree method (MST) and can improve the performance of MST. The contributions of the proposed method are as follows. Firstly, the Euclidean distance weights of edges in MST is improved to be more reasonable. Secondly, the strategy of pruning nodes is updated. Thirdly, separate branches can be merged for broken neurons. FMST and several other reconstruction methods were implemented on the 120 confocal images of single neurons in the Drosophila brain downloaded from the flycircuit database. The performance of FMST is better than some existing methods for some neurons. So it is a potentially practicable neuron construction algorithm. But its performance on some neurons is not good enough and the proposed method still needs to be improved further.
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Acknowledgments
This work is partially supported by the National Basic Research Program of China (No. 2014CB744600), National Natural Science Foundation of China (No. 61420106005), Beijing Natural Science Foundation (No. 4164080), and Beijing Outstanding Talent Training Foundation (No. 2014 000020124G039). The authors thank the BigNeuron project and Dr. Hanchuan Peng for providing the testing image data used in this article and many discussions.
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Hao, M., Yang, J., Liu, X., Wan, Z., Zhong, N. (2016). Fast Marching Spanning Tree: An Automatic Neuron Reconstruction Method. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_6
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DOI: https://doi.org/10.1007/978-3-319-47103-7_6
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