## 1 Introduction  
Electron microscopy (EM) connectomics is an ambitious research direction aimed at studying comprehensive brain connectivity maps using high-throughput, nanoscale microscopes [1]. The development of EM technologies has greatly promoted the progress of brain science and connectomics. Although EM provides sufficient resolution to reveal the invaluable information about structures such as neurons, mitochondria, and synapses [2], higher resolution results in a multiplication of the data volume. As is known to all, it is time-consuming and difficult to annotate large volumes of data manually. Therefore, there is an urgent need to develop automated algorithms to process the structures in EM images. Much effort has been devoted to developing automated algorithms for analyzing the EM data. One of the main application scenarios is neuron segmentation. Recently, [PERSON] et al. [3] used a method called _flood-filling_ to segment and trace neurons in a dataset obtained by serial block-face scanning electron microscopy (SBF-SEM) from a male zebra finch brain. It is a recurrent neural network (RNN)-based method unifying the two steps of finding the boundary between synapses with edge detectors or machine learning classifiers and combining image pixels that are not separated by boundaries using algorithms such as _watershed_ or image segmentation. Due to the important role of mitochondria in cell function, researchers have attempted to quantify important mitochondrial properties in recent years. [PERSON] et al. [4] designed a boosting-based classifier for texture features to detect and segment mitochondria in EM images. [PERSON] et al. [5] considered not only texture features, but also features on the shape of mitochondria. Using the graph cut model, they performed high-precision stereo segmentation of mitochondria on superpixels. Synapses also play an important role in the nervous system, which allows neurons to transmit an electrical or chemical signal to other neurons. [PERSON] et al. [6] reported an automated detection method for synapses from conventionally en-bloc stained 3D electron microscopy image stacks, SynEM. It is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. [PERSON] et al. [7] proposed a deep learning-based method for synapse 3D reconstruction, where the Dijkstra algorithm and GrabCut algorithm were used to segment the synaptic cleft. In addition to the several structures just mentioned, multisynaptic boutons (MSBs) are also worthy of study. It has been proven that MSBs are closely related to sensory deprivation, brain trauma, and learning [8; 9; 10]. MSBs are boutons that make synaptic contacts with multiple postsynaptic structures. They were first seen in cat brain exactly 50 years ago [11] and have been observed in mice, rats, rabbits, cats, and monkeys [8; 12; 13; 14; 15; 16]. Previous work showed that the formation of MSB can be induced by visual sensory deprivation, an enriched environment, brain lesion, motor skill learning, and auditory associative learning [12; 13; 14]. Dendritic spines are tiny protrusions from neuronal dendrites and form the postsynaptic component of the synapse in the brain [17; 18; 19; 20]. Increasing the area or number of dendritic spines can result in more efficacious synaptic transmission and thus enhance the strength of neuronal connections [12; 21; 22]. Both dendritic spines and shafts can form MSBs [17; 23]. Long-term potentiation (LTP), a major form of synapse plasticity, can result in increased spine number and elevated proportion of MSBs in rat hippocampus [12], consistent with the idea that MSB may represent a strengthened form of synaptic connection. Therefore, analyzing the structure and connectivity of MSBs is critical in understanding sensory experience- and learning-associated synaptic plasticity. As mentioned above, MSBs are presynaptic boutons that are in contact with multiple postsynaptic structures.