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Multi-class segmentation of neuronal structures in electron microscopy images

Overview of attention for article published in BMC Bioinformatics, August 2018
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Title
Multi-class segmentation of neuronal structures in electron microscopy images
Published in
BMC Bioinformatics, August 2018
DOI 10.1186/s12859-018-2305-0
Pubmed ID
Authors

Kendrick Cetina, José M. Buenaposada, Luis Baumela

Abstract

Serial block face scanning electron microscopy (SBFEM) is becoming a popular technology in neuroscience. We have seen in the last years an increasing number of works addressing the problem of segmenting cellular structures in SBFEM images of brain tissue. The vast majority of them is designed to segment one specific structure, typically membranes, synapses and mitochondria. Our hypothesis is that the performance of these algorithms can be improved by concurrently segmenting more than one structure using image descriptions obtained at different scales. We consider the simultaneous segmentation of two structures, namely, synapses with mitochondria, and mitochondra with membranes. To this end we select three image stacks encompassing different SBFEM acquisition technologies and image resolutions. We introduce both a new Boosting algorithm to perform feature scale selection and the Jaccard Curve as a tool compare several segmentation results. We then experimentally study the gains in performance obtained when simultaneously segmenting two structures with properly selected image descriptor scales. The results show that by doing so we achieve significant gains in segmentation accuracy when compared to the best results in the literature. Simultaneously segmenting several neuronal structures described at different scales provides voxel classification algorithms with highly discriminating features that significantly improve segmentation accuracy.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 14%
Student > Master 2 14%
Unspecified 1 7%
Student > Ph. D. Student 1 7%
Professor > Associate Professor 1 7%
Other 1 7%
Unknown 6 43%
Readers by discipline Count As %
Computer Science 4 29%
Unspecified 1 7%
Mathematics 1 7%
Biochemistry, Genetics and Molecular Biology 1 7%
Linguistics 1 7%
Other 1 7%
Unknown 5 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 August 2018.
All research outputs
#18,349,015
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#6,088
of 7,400 outputs
Outputs of similar age
#239,857
of 332,427 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 94 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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