Soma Detection in 3D Images of Neurons using Machine Learning Technique
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.
KeywordsSoma detection Machine learning method Drosophila
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.
- Bargmann, C.I. (2012). Beyond the connectome: How neuromodulators shape neural circuits. Bioessays, 34(6), 458–65. http://dx.doi.org/https://doi.org/10.1002/bies.201100185.
- Bishop, C. (2006). Pattern recognition and machine learning (information science and statistics). New York: Springer.Google Scholar
- Chiang, A.S., Lin, C.Y., Chuang, C.C., Chang, H.M., Hsieh, C.H., Yeh, C.W., Shih, C.T., Wu, J.J., Wang, G.T., Chen, Y.C., Wu, C.C., Chen, G.Y., Ching, Y.T., Lee, P.C., Lin, C.Y., Lin, H.H., Wu, C.C., Hsu, H.W., Huang, Y.A., Chen, J.Y., Chiang, H.J., Lu, C.F., Ni, R.F., Yeh, C.Y., & Hwang, J.K. (2011). Three-dimensional reconstruction of brain-wide wiring networks in drosophila at single-cell resolution. Current Biology, 21, 1–11. https://doi.org/10.1016/j.cub.2010.11.056.CrossRefPubMedGoogle Scholar
- Cohen, A.R., Roysam, B., & Turner, J.N. (1994). Automated tracing and volume measurements of neurons from 3-d confocal fluorescence microscopy data. Journal of Microscopy, 173 (Pt2), 103–114. https://doi.org/10.1111/j.1365-2818.1994.tb03433.x.CrossRefPubMedGoogle Scholar
- Ho, S.Y., Chao, C.Y., Huang, H.L., Chiu, T.W., Charoenkwan, P., & Hwang, E. (2011). Neurphologyj: an automatic neuronal morphology quantification method and its application in pharmacological discovery. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-12-230.
- Kim, K.M., Son, K., & Palmore, G.T.R. (2015). Neuron image analyzer: Automated and accurate extraction of neuronal data from low quality images. Scientific Reports, 5, 17062. https://doi.org/10.1038/srep17062.
- Lee, P.C., Chuang, C.C., Chiang, A.S., & Ching, Y.T. (2012). Highthroughput computer method for 3d neuronal structure reconstruction from the image stack of the drosophila brain and its applications. PLoS Computational Biology, 8(9), e1002,658. https://doi.org/10.1371/journal.pcbi.1002658.CrossRefGoogle Scholar
- Liu, S., Zhang, D., Song, Y., Peng, H., & Cai, W. (2017). Automated 3d neuron tracing with precise branch erasing and confidence controlled back-tracking. bioRxiv. https://doi.org/10.1101/109892.
- Shih, C.T., Sporns, O., Yuan, S.L., Su, T.S., Lin, Y.J., Chuang, C.C., Wang, T.Y., Lo, C.C., Greenspan, R.J., & Chiang, A. S. (2015). Connectomics-based analysis of information flow in the drosophila brain. Current Biology, 25(10), 1249–58. https://doi.org/10.1016/j.cub.2015.03.021.CrossRefPubMedGoogle Scholar
- Snyman, J. (2005). Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms. Berlin: Springer Publishing.Google Scholar
- Sui, D., Wang, K., Chae, J., Zhang, Y., & Zhang, H. (2014). A pipeline for neuron reconstruction based on spatial sliding volume filter seeding. Computational and mathematical methods in medicines https://doi.org/10.1155/2014/386974.
- Zhang, D., Liu, S., Liu, S., Feng, D., Peng, H., & Cai, W. (2016). Sub-voxel reconstruction of 3d neuron morphology using rivulet back-tracking. The IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2016).Google Scholar
- Zhou, Z., Liu, X., Long, B., & Peng, H. (2016). Tremap automatic 3d neuron reconstruction based on tracing, reverse mapping and assembling of 2d projections. Frontiers Neuroinform 14(1), 41–50. https://doi.org/10.1007/s12021-015-9278-1.