DIRT/mu: Automatic root hair measurement in maize (Zea mays ssp.) from microscopy images

Pietrzyk, Peter; Phan-Udom, Neen; Chutoe, Chartinun; Saengwilai, Patompong; Bucksch, Alexander

doi:10.1002/essoar.10508833.1

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PlantIT and CyVerse are easily accessible to researchers without any programming knowledge 46 . Online workshops are available for end users for training through PhenomeForce 47 .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

Pietrzyk,

Phan-Udom,

Chutoe

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Similar to any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional (2D) images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) addresses this issue by computationally resolving intersections and extracting individual root hairs from 2D microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length (RHL) distribution and root hair density (RHD), which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics.

show abstract

“…PlantIT and CyVerse are easily accessible to researchers without any programming knowledge 46 . Online workshops are available for end users for training through PhenomeForce 47 .…”

Section: Discussionmentioning

confidence: 99%

“…DIRT/μ provides high-quality and affordable root hair phenotyping with good availability, accessibility, and applicability for all research labs around the world. DIRT/μ is available on GitHub 44 and as a Docker container 45 . PlantIT and CyVerse are easily accessible to researchers without any programming knowledge 46 .…”

Section: Discussionmentioning

confidence: 99%

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

Pietrzyk,

Phan-Udom,

Chutoe

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

DIRT/mu: Automatic root hair measurement in maize (Zea mays ssp.) from microscopy images

Cited by 1 publication

References 11 publications

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

Contact Info

Product

Resources

About