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Segmentation of 3D images of plant tissues at multiple scales using the level set method

Overview of attention for article published in Plant Methods, December 2017
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2 tweeters

Citations

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10 Dimensions

Readers on

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43 Mendeley
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Title
Segmentation of 3D images of plant tissues at multiple scales using the level set method
Published in
Plant Methods, December 2017
DOI 10.1186/s13007-017-0264-5
Pubmed ID
Authors

Annamária Kiss, Typhaine Moreau, Vincent Mirabet, Cerasela Iliana Calugaru, Arezki Boudaoud, Pradeep Das

Abstract

Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue. We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue. The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 10 23%
Student > Master 5 12%
Professor > Associate Professor 3 7%
Student > Bachelor 3 7%
Other 5 12%
Unknown 6 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 30%
Biochemistry, Genetics and Molecular Biology 9 21%
Computer Science 7 16%
Engineering 4 9%
Physics and Astronomy 2 5%
Other 0 0%
Unknown 8 19%

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 22 December 2017.
All research outputs
#7,716,895
of 12,341,991 outputs
Outputs from Plant Methods
#340
of 478 outputs
Outputs of similar age
#193,779
of 347,185 outputs
Outputs of similar age from Plant Methods
#32
of 50 outputs
Altmetric has tracked 12,341,991 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 478 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 19th percentile – i.e., 19% 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 347,185 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.