Automatic Classification for the Type of Multiple Synapse Based on Deep Learning

Abstract

Recent studies have shown that the synaptic plasticity induced by development and learning can promote the formation of multiple synapse. With the rapid development of electron microscopy (EM) technology, we can closely observe the multiple synapse structure with high resolution. Although the multiple synapse has been widely researched by recent researchers, the classification accuracy for the type of multiple synapse has not been documented. In this paper, we propose an effective automatic classification method for the type of multiple synapse. The main steps are summarized as three parts: synaptic cleft segmentation, vesicle band segmentation, multiple synapse classification. The experiments on four datasets demonstrate that the proposed method can reach an average accuracy about 97%.