## 5 Discussion  
As shown in Figure 15, three types of MSBs were identified in mouse cortex: (1) axon-multiple dendritic shaft synapse, a single axon presynaptic terminal forming synapses with two or more postsynaptic dendritic shafts; (2) axon-multiple dendritic spine synapse, a single axon presynaptic bouton forming synaptic contact with two or more postsynaptic dendritic spines; (3) axon-dendritic shafts-dendritic spine, including a single axon terminal site forming synapses with one or more dendritic shafts and one or more dendritic spines. [PERSON] et al. suggested that different types of MSBs have disparate effects on cerebellar synaptic transmission [40]. Therefore, it makes sense to classify the MSBs in more detail. For example, in many cases, it is necessary to analyze different groups of biological tissue data grouped by age or drug variables to further explore the role of different factors. Our method can be used to analyze the differences in the number or distribution of MSBs in different groups. Especially when the amount of data is large, the advantages of this automatic method are more obvious. Except for ATUM-SEM, current mainstream EM technologies include serial block-face scanning electron microscopy (SBF-SEM), serial section transmission electron microscopy (ssTEM), and focused ion beam scanning electron microscopy (FIB-SEM). Although each of these methods has its pros and cons, they all provide sufficient resolution to reveal invaluable information about structures such as neurons, mitochondria, and synapses. Therefore, our approach is also suitable for EM images of these types and data from other biological tissues.  
Figure 14: Partial recognition results of MSB. From left to right: raw image, segmentation of neuron, segmentation of synapses, and the comparison of the segmentation of synapses and the ground truth of synapses, where the yellow pixels represent true positives, red pixels represent false positives, and green pixels represent false negatives.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Type A** & **Type B** & **Type C** & **Total** \\ \hline Numbers & 946 & 50 & 7 & 1003 \\ Ratio & 94.31\% & 4.99\% & 0.7\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of different types of MSBs.  
Though the recognition accuracy of our method was acceptable under the premise of a high recall rate, which is of great help to biologists' research on MSBs, the proposed method will also fail in some cases. As shown in Figure 16, the MSBs were incorrectly identified in the same area of three adjacent images. Due to the misdetection of synapses on more than two serial images, our algorithm for deleting pseudo-synapses and locating missed synapses failed to remove pseudo-synapses and even mistakenly added synapses on the image \"Section 122\". This was caused by the fact that the edges of soma were too similar in morphology to the synapses, which made them difficult to distinguish. In order to solve this problem, we needed to improve the robustness of the algorithm in synaptic detection. Similarly, over-segmentation or under-segmentation of neurons can also lead to missed or misdetected MSBs.  
As mentioned above, our method relied on synaptic detection and neuron segmentation, so improving algorithm performance was one of our directions for improvement. Meanwhile, two networks that handled synapses and membrane structures separately were trained in this study. From the perspective of complexity, if the two networks can be combined into one, that is designing a multi-class segmentation network, the efficiency of the recognition algorithm would be greatly improved. The difficulty is that the synaptic cleft is located on the membrane of the neuron and is very prone to confusion.