High-throughput phenotyping methods for quantifying hair fiber morphology

Tina Lasisi, Arslan A. Zaidi, Timothy H. Webster, Nicholas B. Stephens, Kendall Routch, Nina G. Jablonski, Mark D. Shriver

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Quantifying the continuous variation in human scalp hair morphology is of interest to anthropologists, geneticists, dermatologists and forensic scientists, but existing methods for studying hair form are time-consuming and not widely used. Here, we present a high-throughput sample preparation protocol for the imaging of both longitudinal (curvature) and cross-sectional scalp hair morphology. Additionally, we describe and validate a new Python package designed to process longitudinal and cross-sectional hair images, segment them, and provide measurements of interest. Lastly, we apply our methods to an admixed African-European sample (n = 140), demonstrating the benefit of quantifying hair morphology over classification, and providing evidence that the relationship between cross-sectional morphology and curvature may be an artefact of population stratification rather than a causal link.

Original languageEnglish (US)
Article number11535
JournalScientific reports
Issue number1
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • General


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