类脑智能研究中心知识库

Serial-section electron microscopy is a widely used technique for neuronal circuit reconstruction. However, the continuity of neuronal structure is destroyed when the tissue block is cut into a series of sections. The neuronal morphology in different sections changes with their locations in the tissue block. These content changes in adjacent sections bring a diffculty to the registration of serial electron microscopy images. As a result, the
accuracy of image registration is strongly influenced by neuronal structure variation and section thickness. 
To evaluate registration performance, we use the spherical deformation model as a simulation of the neuron structure to analyze how registration accuracy is affected by section thickness and neuronal structure size. We mathematically describe the trend that the correlation of neuronal structures in two adjacent sections decreases with section thickness. Furthermore, we demonstrate that registration accuracy is negatively correlated with neuronal structure size and section thickness by analyzing the second-order moment of estimated translation. The experimental results of registration on synthetic data demonstrate that registration accuracy decreases with the neuronal structure size.