A method for identification of multisynaptic boutons in electron microscopy image stack of mouse cortex

Abstract

Recent electron microscopy (EM) imaging techniques make the automatic acquisition of a large number of serial sections from brain samples possible. On the other hand, it has been proven that the multisynaptic bouton (MSB), a structure that consists of one presynaptic bouton and multiple postsynaptic spines, is closely related to sensory deprivation, brain trauma, and learning. Nevertheless, it is still a challenging task to analyze this essential structure from EM images due to factors such as imaging artifacts and the presence of complicated subcellular structures. In this paper, we present an effective way to identify the MSBs on EM images. Using normalized images as training data, two convolutional neural networks (CNNs) are trained to obtain the segmentation of synapses and the probability map of the neuronal membrane, respectively. Then, a series of follow-up operations are employed to obtain rectified segmentation of synapses and segmentation of neurons. By incorporating this information, the MSBs can be reasonably identified. The dataset in this study is an image stack of mouse cortex that contains 178 serial images with a size of 6004 pixels × 5174 pixels and a voxel resolution of 2 nm × 2 nm × 50 nm. The precision and recall on MSB detection are 68.57% and 94.12%, respectively. Experimental results demonstrate that our method is conducive to biologists' research on MSBs' properties.