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.
Highlights
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
Inspired by the previous work focusing on bio-electron image processing, we propose an efficient way to identify the multisynaptic bouton (MSB) from serial EM images
We presented an approach for identifying MSBs on a serial EM image stack of mouse cortex
Summary
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. 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. 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
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