Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics

doi:10.1109/ISBI.2018.8363597

CASIA OpenIR > 脑图谱与类脑智能实验室 > 微观重建与智能分析

Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics

Xiao C(肖驰)1,2

; Liu J(刘静)1,2

; Chen X(陈曦)1

; Han H(韩华)1,2,3

; Shu C(舒畅)1

; Xie QW(谢启伟)1

2018-06

会议名称

IEEE International Symposium on Biomedical Imaging (ISBI)

会议日期

美国华盛顿

会议地点

2018-4

摘要

The goal of connectomics research is to manifest the mechanisms and functions of neural system by using electron microscopy (EM). One of the biggest challenges in connectomic reconstruction is developing reliable neuronal membranes segmentation method to reduce the burden on manual neurite labeling and validation. In this paper, we put forward an effective deep learning approach to realize neuronal membranes segmentation in EM image stacks, which utilizes spatially efficient residual network and multilevel representations of contextual cues to achieve accurate segmentation performance. Furthermore, multicut is used as post-processing to optimize the outputs of network. Experimental results on the public dataset of ISBI 2012 EM Segmentation Challenge demonstrate the effectiveness of our approach in neuronal membranes segmentation. Our method now ranks top 3 among 88 teams and yields 0.98356 Rand Score as well as 0.99063 Information Score, which outperforms most of state-of-the-art methods.

关键词

Connectomics, Deep Learning, Image Segmentation, Electron Microscopy

DOI

10.1109/ISBI.2018.8363597

收录类别

资助项目

Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Strategic Priority Research Program of the CAS[XDB02060001] ; National Natural Science Foundation of China[31472001] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[31472001] ; Strategic Priority Research Program of the CAS[XDB02060001] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146]

语种

英语

引用统计

文献类型

会议论文

条目标识符

http://ir.ia.ac.cn/handle/173211/23697

专题

脑图谱与类脑智能实验室_微观重建与智能分析
类脑智能研究中心

通讯作者

Han H(韩华)

作者单位

1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
3.The Center for Excellence in Brain Science and Intelligence Technology, CAS, Shanghai, China

第一作者单位

中国科学院自动化研究所

通讯作者单位

中国科学院自动化研究所

推荐引用方式
GB/T 7714

Xiao C,Liu J,Chen X,et al. Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics[C],2018.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Deep Contextual Resi（614KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载