A novel 3D instance segmentation network for synapse reconstruction from serial electron microscopy images

• Published in: Expert systems with applications 2024-12, Vol.255, p.124562, Article 124562
• Main Authors: Liu, Jing, Hong, Bei, Xiao, Chi, Zhai, Hao, Shen, Lijun, Xie, Qiwei, Han, Hua
• Format: Article
• Language: English
• Subjects: Connectomics; Deep learning; Electron microscopy; Instance segmentation; Synapse
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.124562

Synapses are fundamental components of how neurons communicate with each other and have attracted widespread attention from neuroscientists. Due to the rapid development of electron microscopy (EM) technology, imaging synapses at nanometer scale has become possible. However, the automation and efficacy of the synapse detection algorithm have not yet met expectations. The most popular approach involves a two-step process in which binary segmentation masks are first obtained and then connected components are used to produce reconstruction results. In this paper, an intelligent system to detect and segment synapses from serial section EM images is proposed. Specifically, a novel 3D instance segmentation network that can predict the synapses end-to-end is presented. The network can exploit and summarize features consistent with the biological structures of synapses, which is similar to the process of manual annotation. A block-wise inference strategy that adapts well to large-scale EM images is then introduced. Finally, two public datasets are used to evaluate our method. Experimental results demonstrate the superiority of the proposed approach, thus enabling computer-assisted analysis of synapses for neuroscientists. •An intelligent region-based 3D instance segmentation system of synapses.•Effective feature representation by fusing semantic with instance features.•The network can learn distinctive biological features of concern.•Experimental results on two public datasets show promising performances.