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MowJoe: a method for automated-high throughput dissected leaf phenotyping

Overview of attention for article published in Plant Methods, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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Title
MowJoe: a method for automated-high throughput dissected leaf phenotyping
Published in
Plant Methods, March 2018
DOI 10.1186/s13007-018-0290-y
Pubmed ID
Authors

Henrik Failmezger, Janne Lempe, Nasim Khadem, Maria Cartolano, Miltos Tsiantis, Achim Tresch

Abstract

Accurate and automated phenotyping of leaf images is necessary for high throughput studies of leaf form like genome-wide association analysis and other forms of quantitative trait locus mapping. Dissected leaves (also referred to as compound) that are subdivided into individual units are an attractive system to study diversification of form. However, there are only few software tools for their automated analysis. Thus, high-throughput image processing algorithms are needed that can partition these leaves in their phenotypically relevant units and calculate morphological features based on these units. We have developed MowJoe, an image processing algorithm that dissects a dissected leaf into leaflets, petiolule, rachis and petioles. It employs image skeletonization to convert leaves into graphs, and thereafter applies algorithms operating on graph structures. This partitioning of a leaf allows the derivation of morphological features such as leaf size, or eccentricity of leaflets. Furthermore, MowJoe automatically places landmarks onto the terminal leaflet that can be used for further leaf shape analysis. It generates specific output files that can directly be imported into downstream shape analysis tools. We applied the algorithm to two accessions of Cardamine hirsuta and show that our features are able to robustly discriminate between these accessions. MowJoe is a tool for the semi-automated, quantitative high throughput shape analysis of dissected leaf images. It provides the statistical power for the detection of the genetic basis of quantitative morphological variations.

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The data shown below were collected from the profiles of 33 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 15%
Researcher 3 9%
Student > Master 3 9%
Other 2 6%
Professor > Associate Professor 2 6%
Other 3 9%
Unknown 15 45%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 36%
Biochemistry, Genetics and Molecular Biology 4 12%
Environmental Science 1 3%
Neuroscience 1 3%
Unknown 15 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 15 May 2018.
All research outputs
#1,614,072
of 24,088,850 outputs
Outputs from Plant Methods
#65
of 1,152 outputs
Outputs of similar age
#36,151
of 334,032 outputs
Outputs of similar age from Plant Methods
#1
of 24 outputs
Altmetric has tracked 24,088,850 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,152 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 94% of its peers.
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 334,032 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.