Acknowledgements

The course

This module is taught by the Single Cell Facility of D-BSSE, ETH Zurich, as part of the Lab Course Methods in Cell Analysis and Laboratory Automation.

A large part of the material derives, with permission, from teaching developed over many years by Aaron Ponti, and from earlier editions of this course prepared with Andreas Cuny. The iaf library that the homework relies on is their work.

The data

All example images come from the Broad Bioimage Benchmark Collection, a public collection of annotated image sets maintained for exactly this kind of use. Having expert annotations to compare against is what makes day 2 possible.

The collection as a whole should be cited as:

Ljosa V, Sokolnicki KL, Carpenter AE (2012). Annotated high-throughput microscopy image sets for validation. Nature Methods 9(7):637.

The individual sets used here, with their source citations:

BBBC020: murine bone-marrow derived macrophages, DAPI and CD11b. The guiding dataset for both days.

“We used image set BBBC020 from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].”

BBBC010: C. elegans live/dead assay. Used for the detective game.

“We used the C. elegans infection live/dead image set version 1 provided by Fred Ausubel and available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].”

BBBC030: Chinese hamster ovary cells in DIC.

“We used image set BBBC030v1 [Koos, K., Molnár, J., Kelemen, L., Tamás, G., & Horvath, P. (2016). DIC image reconstruction using an energy minimization framework to visualize optical path length distribution. Scientific Reports, 6.] from the Broad Bioimage Benchmark Collection.”

Full provenance for every file, its source, the licence, and exactly what processing was applied, is recorded in data/MANIFEST.md.

Note

The BBBC images are cropped and downscaled for teaching. If you use any of them in your own work, go back to the originals: the versions here have been altered.

The software

This course would not be possible without scikit-image, NumPy, matplotlib, pandas, SciPy, Fiji, Cellpose, napari and Jupyter.

If you rely on one of these tools in your own work, cite it to credit its developers and maintainers.