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An Automatic Classification Pipeline for the Complex Synaptic Structure Based on Deep Learning

SHEN Lijun1, MA Chao1, LUO Jie2, HONG Bei2   

  1. 1. Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China;
    2. Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China
  • Received:2020-12-04 Revised:2021-06-02 Online:2022-08-25 Published:2022-08-02
  • Supported by:
    This research was supported by the Science and Technology Development Fund, Macau SAR under Grant No. 0024/2018/A1, the Research Fund of Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent MicroNano Optoelectronic Technology under Grant No. 2020B1212030010, the National Science Foundation of China under Grant Nos. 61673381, 61201050, 61306070, 61701497, 11771130, 61871177, Special Program of Beijing Municipal Science & Technology Commission under Grant No. Z181100000118002, Strategic Priority Research Program of Chinese Academy of Science under Grant No. XDB32030200, and Scientific research instrument and equipment development project of Chinese Academy of Sciences under Grant No. YZ201671.

SHEN Lijun, MA Chao, LUO Jie, HONG Bei. An Automatic Classification Pipeline for the Complex Synaptic Structure Based on Deep Learning[J]. Journal of Systems Science and Complexity, 2022, 35(4): 1398-1414.

As a hallmark of brain complexity, synapses in the nervous system have always received extensive attentions. The diversity of the synaptic structure reflects various functions and mechanisms, some research indicates that, as one of the complex synaptic structures, multiple synapses can strengthen the synaptic connection, what's more, it is closely associated with the procedure of memory and learning. Accompanied by the fast advancement of electron microscopy (EM) technology, it is possible to detect the composition of multiple synapse with high resolution. On this basis, there have been various meaningful studies concerning the relationship between the multiple synapse and cognitive abilities. Despite the extensive studies have been made by different researchers on multiple synapse, no attention has been paid to the classification accuracy of the type of multiple synapse. The current research puts forward an effective method for the automatic classification of multiple synapse, which should be performed in three steps, namely the segmentation of synaptic clefts, the segmentation of vesicle bands, as well as the segmentation of multiple synapses. According to experimental results based on four data sets, the mean classification rate of the method is approximately 97%. In addition, the experimental result on the public dataset shows that the accuracy can reach 96.5%. The classification results provide a basis for quantitative statistics of follow-up studies. Moreover, this automatic classification method can reduce the time in artificial statistics, and thus researchers can focus more attention on the analysis of statistical results.
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