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Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Overview of attention for article published in Frontiers in Neuroanatomy, November 2018
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Title
Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network
Published in
Frontiers in Neuroanatomy, November 2018
DOI 10.3389/fnana.2018.00092
Pubmed ID
Authors

Chi Xiao, Xi Chen, Weifu Li, Linlin Li, Lu Wang, Qiwei Xie, Hua Han

Abstract

<p>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.</p>

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 22%
Researcher 9 12%
Student > Master 9 12%
Student > Bachelor 3 4%
Student > Postgraduate 2 3%
Other 7 9%
Unknown 31 40%
Readers by discipline Count As %
Computer Science 11 14%
Biochemistry, Genetics and Molecular Biology 5 6%
Engineering 5 6%
Agricultural and Biological Sciences 4 5%
Physics and Astronomy 3 4%
Other 17 22%
Unknown 33 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 August 2021.
All research outputs
#13,631,076
of 23,112,054 outputs
Outputs from Frontiers in Neuroanatomy
#576
of 1,170 outputs
Outputs of similar age
#177,409
of 351,130 outputs
Outputs of similar age from Frontiers in Neuroanatomy
#21
of 38 outputs
Altmetric has tracked 23,112,054 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,170 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,130 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.