EuroSciPy 2024

Scientists are producing more and more images with telescopes, microscopes, MRI scanners, etc. They need automatable tools to measure what they've imaged and help them turn these images into knowledge. This tutorial covers the fundamentals of algorithmic image analysis, starting with how to think of images as NumPy arrays, moving on to basic image filtering, and finishing with a complete workflow: segmenting a 3D image into regions and making measurements on those regions.

The conversation about reproducibility is usually focused on how to make research workflows (more) reproducible. Here, we consider it from the opposite perspective, and ask: How feasible is it, in practice, to reproduce research which is meant to be reproducible? Is it even done or attempted? We provide a detailed account of such an attempt, trying to reproduce some segmentation results for 3D microscopy images of a developing mouse embryo. The original research is a monumental work of bioimaging and analysis at the single-cell level, published in Cell in 2018, alongside with all the necessary research artifacts. Did we succeed in this attempt? As we share the joys and pains of this journey, many questions arise: How do reviewers assess the reproducibility claims exactly? Incentivizing reproducible research is still an open problem, since it is so much more costly (in time) to produce. And how can we incentivize those who test reproducibility? Not only is it costly to set up computational environments and execute data-intensive scientific workflows, but it may not appear as rewarding at first thought. In addition, there is a human factor: It is thorny to show authors that their publication does not hold up to their reproducibility claims.