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An effective fully deep convolutional neural network for mitochondria segmentation based on ATUM-SEM
Xiao C(肖驰)1,2; Li WF(李伟夫)1,3; Chen X(陈曦)1; Han H(韩华)1,2,4; Xie QW(谢启伟)1
2018-03
Conference NameSPIE Medical Imaging
Conference Date2018-2
Conference Place美国休斯敦
Abstract
Recent studies have empowered that the relation between mitochondrial function and degenerative disorders is related to aging diseases. Due to the rapid development of electron microscope (EM), stacks delivered by EM can be used to investigate the link between mitochondrial function and physical structure. Whereas, one of the main challenges in mitochondria research is developing suitable segmentation algorithms to obtain the shapes of mitochondria. Nowadays, Deep Neural Network (DNN) has been widely applied in solving the neuron membrane segmentation problems in virtue of its exceptional performance. For this reason, its application to mitochondria segmentation holds great promise. In this paper, we propose an effective deep learning approach to realize mitochondria segmentation in Automated Tape-Collecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) stacks. The proposed algorithm contains three parts: (1) utilizing histogram equalization algorithm as image preprocessing to keep the consistency of dataset; (2) putting forward a fusion fully convolution network (FCN), which is motivated by the principle the deeper, the better, to build a much deeper network architecture for more accurate mitochondria segmentation; and (3) employing fully connected conditional random field (CRF) to optimize segmentation results. Evaluation was performed on a dataset of a stack of 31 slices from ATUM-SEM, with 20 images used for training and 11 images for testing. For comparison, U-Net approach was evaluated through the same dataset. Jaccard index between the automated segmentation and expert manual segmentations indicates that our method (90.1%) outperforms U-Net (87.9%) and has a preferable performance on mitochondria segmentation with different shapes and sizes.
KeywordElectron Microscope, Deep learning, Mitochondria segmentation
DOIhttps://doi.org/10.1117/12.2293291
Indexed ByEI
Funding ProjectScientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146]
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23695
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorHan H(韩华)
Affiliation1.Chinese Academy of Sciences, Institute of Automation, Beijing, China
2.University of Chinese Academy of Sciences, School of Future Technology, Beijing, China
3.Hubei University, Faculty of Mathematics and Statistic, Wuhan, China
4.The Center for Excellence in Brain Science and Intelligence Technology, CAS, 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 C,Li WF,Chen X,et al. An effective fully deep convolutional neural network for mitochondria segmentation based on ATUM-SEM[C],2018.
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