Abstract

Info

Title:: Segmentation of neural structure in electron microscopy image based on Group-Depth U-Net

Author(s):: LI Yuhui; LIANG Chuangxue; LI Jun; School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China

Keywords:: Keywords: deep convolutional neural network group convolutional network neural structure segmentation electron microscopy imaging Group-Depth U-Net

Abstract:: Abstract: Aiming at the problems of electron microscopy (EM) imaging, such as boundary damage, fuzzy inhomogeneity and difficulty of localization due to the complex contour texture of neural structure itself, a deep convolutional neural network model, Group-Depth U-Net, is proposed to realize automatic segmentation of neural structure in EM image. In the proposed model, a deeper U-Net architecture is used as the backbone network to obtain more abundant image feature information. Meanwhile, group convolutional network structure is adopted to make the model more efficient and prevent over-fitting, thus improving the accuracy and efficiency of segmentation. The experiments on the open data set show that the proposed model achieves a higher segmentation accuracy than U-Net.