Robust extraction of quantitative structural information from high-variance histological images of livers from necropsied soay sheep

Q. Caudron, R. Garnier, J. G. Pilkington, K. A. Watt, C. Hansen, Bryan T. Grenfell, T. Aboellail, Andrea Linn Graham

Research output: Contribution to journalArticle

Abstract

Quantitative information is essential to the empirical analysis of biological systems. In many such systems, spatial relations between anatomical structures is of interest, making imaging a valuable data acquisition tool. However, image data can be difficult to analyse quantitatively. Many image processing algorithms are highly sensitive to variations in the image, limiting their current application to fields where sample and image quality may be very high. Here, we develop robust image processing algorithms for extracting structural information from a dataset of high-variance histological images of inflamed liver tissue obtained during necropsies of wild Soay sheep. We demonstrate that features of the data can be measured in a fully automated manner, providing quantitative information which can be readily used in statistical analysis. We show that these methods provide measures that correlate well with a manual, expert operator-led analysis of the same images, that they provide advantages in terms of sampling a wider range of information and that information can be extracted far more quickly than in manual analysis.

Original languageEnglish (US)
Article number170111
JournalRoyal Society Open Science
Volume4
Issue number7
DOIs
StatePublished - Jul 19 2017

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Computer-aided diagnostics
  • Disease ecology
  • Histopathology
  • Ovis aries
  • Quantitative histology

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