DIRT/µ: automated extraction of root hair traits using combinatorial optimization
Issued Date
2025-01-10
Resource Type
ISSN
00220957
eISSN
14602431
Scopus ID
2-s2.0-85215215858
Journal Title
Journal of Experimental Botany
Volume
76
Issue
2
Start Page
285
End Page
298
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Experimental Botany Vol.76 No.2 (2025) , 285-298
Suggested Citation
Pietrzyk P., Phan-Udom N., Chutoe C., Pingault L., Roy A., Libault M., Saengwilai P.J., Bucksch A. DIRT/µ: automated extraction of root hair traits using combinatorial optimization. Journal of Experimental Botany Vol.76 No.2 (2025) , 285-298. 298. doi:10.1093/jxb/erae385 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/103028
Title
DIRT/µ: automated extraction of root hair traits using combinatorial optimization
Corresponding Author(s)
Other Contributor(s)
Abstract
As with phenotyping of 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 images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) is a newly developed algorithm that addresses this issue by computationally resolving intersections and extracting individual root hairs from two-dimensional 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 distribution and root hair density, 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.