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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
Source PublicationFRONTIERS IN NEUROANATOMY
ISSN1662-5129
2018-11-02
Issue12Pages:92
Abstract

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.

Keywordelectron microscope deep learning volumetric mitochondria segmentation mitochondria morphology neuroinformatics
DOI10.3389/fnana.2018.00092
WOS KeywordSCANNING-ELECTRON-MICROSCOPY ; CANCER ; IMAGES ; BRAIN ; RECONSTRUCTION ; SHAPE
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaAnatomy & Morphology ; Neurosciences & Neurology
WOS SubjectAnatomy & Morphology ; Neurosciences
WOS IDWOS:000449098100001
PublisherFRONTIERS MEDIA SA
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22772
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorXie, Qiwei; Han, Hua
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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