Name
Title | ||
|---|---|---|
An Automatic Classification Pipeline for the Complex Synaptic Structure Based on Deep Learning |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11424-022-0307-5 | Journal of Systems Science and Complexity |
Keywords | DocType | Volume |
Classification, deep learning, electron microscopy, multiple synapses, synaptic cleft, vesicle band | Journal | 35 |
Issue | ISSN | Citations |
4 | 1009-6124 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| SHEN Lijun | 1 | 0 | 0.34 |
| Chao Ma | 2 | 85 | 27.49 |
| Jie Luo | 3 | 706 | 73.44 |
| HONG Bei | 4 | 0 | 0.34 |