Abstract
Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.
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Acknowledgments
We thank the staff of the National Center for High-Performance Computing, Hsinchu, Taiwan, for their help with data maintenance. This work was supported by a grant from Ministry of Science and Technology of Taiwan (MOST-04-2221-E-009-165). The authors are also grateful to Dr. Chi-Tin Shih and Dr. Nan-Yow Chen for their helping in providing the concepts.
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The algorithm of soma detection is implemented in C++ and the feature obtained code is available at https://github.com/wilsonGW/SomaDetection(RRID:SCR_015717). The data are included in the FlyCircuit Database (RRID:SCR_006375). Available at: http://www.flycircuit.tw.
These authors contributed equally to this work.
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He, GW., Wang, TY., Chiang, AS. et al. Soma Detection in 3D Images of Neurons using Machine Learning Technique. Neuroinform 16, 31–41 (2018). https://doi.org/10.1007/s12021-017-9342-0
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DOI: https://doi.org/10.1007/s12021-017-9342-0