Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network | |
Xiao, Chi1,2; Chen, Xi1; Li, Weifu3; Li, Linlin1; Wang, Lu4; Xie, Qiwei1,5; Han, Hua1,2,6 | |
发表期刊 | FRONTIERS IN NEUROANATOMY |
ISSN | 1662-5129 |
2018-11-02 | |
期号 | 12页码:92 |
摘要 | Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria. |
关键词 | electron microscope deep learning volumetric mitochondria segmentation mitochondria morphology neuroinformatics |
DOI | 10.3389/fnana.2018.00092 |
关键词[WOS] | SCANNING-ELECTRON-MICROSCOPY ; CANCER ; IMAGES ; BRAIN ; RECONSTRUCTION ; SHAPE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Strategic Priority Research Program of the CAS[XDB02060001] ; Strategic Priority Research Program of the CAS[XDB02060001] ; Scientific Research Instrument and Equipment Development Project of the CAS[YZ201671] ; Scientific Research Instrument and Equipment Development Project of the CAS[YZ201671] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Special Program of Beijing Municipal Science and Technology Commission[Z161100000216146] ; Special Program of Beijing Municipal Science and Technology Commission[Z161100000216146] ; National Natural Science Foundation of China[31472001] ; National Natural Science Foundation of China[31472001] ; National Natural Science Foundation of China[61871177] ; National Natural Science Foundation of China[61871177] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[11771130] ; National Natural Science Foundation of China[11771130] ; National Natural Science Foundation of China[11771130] ; National Natural Science Foundation of China[11771130] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[61871177] ; National Natural Science Foundation of China[61871177] ; National Natural Science Foundation of China[31472001] ; National Natural Science Foundation of China[31472001] ; Special Program of Beijing Municipal Science and Technology Commission[Z161100000216146] ; Special Program of Beijing Municipal Science and Technology Commission[Z161100000216146] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Scientific Research Instrument and Equipment Development Project of the CAS[YZ201671] ; Scientific Research Instrument and Equipment Development Project of the CAS[YZ201671] ; Strategic Priority Research Program of the CAS[XDB02060001] ; Strategic Priority Research Program of the CAS[XDB02060001] |
WOS研究方向 | Anatomy & Morphology ; Neurosciences & Neurology |
WOS类目 | Anatomy & Morphology ; Neurosciences |
WOS记录号 | WOS:000449098100001 |
出版者 | FRONTIERS MEDIA SA |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22772 |
专题 | 类脑智能研究中心_微观重建与智能分析 |
通讯作者 | Xie, Qiwei; Han, Hua |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China 3.Hubei Univ, Fac Math & Stat, Wuhan, Hubei, Peoples R China 4.Peking Univ, Acad Adv Interdisciplinary Studies, Beijing, Peoples R China 5.Beijing Univ Technol, Data Min Lab, Beijing, Peoples R China 6.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R 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, Chi,Chen, Xi,Li, Weifu,et al. Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network[J]. FRONTIERS IN NEUROANATOMY,2018(12):92. |
APA | Xiao, Chi.,Chen, Xi.,Li, Weifu.,Li, Linlin.,Wang, Lu.,...&Han, Hua.(2018).Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network.FRONTIERS IN NEUROANATOMY(12),92. |
MLA | Xiao, Chi,et al."Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network".FRONTIERS IN NEUROANATOMY .12(2018):92. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Automatic Mitochondr(3873KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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