Luo Jie, Hong Bei, Jiang Yi, Li Linlin, Xie Qiwei, Han Hua
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:40-43. doi: 10.1109/EMBC.2019.8856509.
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%.
最近的研究表明，由发育和学习诱导的突触可塑性可以促进多个突触的形成。随着电子显微镜（EM）技术的迅速发展，我们能够以高分辨率密切观察多个突触的结构。尽管最近的研究人员对多个突触进行了广泛研究，但关于多个突触类型的分类准确率尚未见报道。在本文中，我们提出了一种有效的多个突触类型自动分类方法。主要步骤总结为三个部分：突触间隙分割、囊泡带分割、多个突触分类。在四个数据集上的实验表明，所提出的方法平均准确率可达约97%。
