Made to measure: an introduction to quantification in microscopy data

TL;DR

This paper introduces key concepts and considerations for quantifying microscopy data, emphasizing the importance of aligning measurement types with specific biological questions to ensure meaningful analysis.

Contribution

It provides a comprehensive overview of measurement types in microscopy and offers a toolkit for critically assessing quantitative bioimage analysis methods.

Findings

Identifies three main types of visual information: intensity, morphology, and object counts.

Discusses factors affecting measurement relevance in biological contexts.

Highlights the importance of aligning measurements with biological questions.

Abstract

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of visual information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement "good" is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.