Further reading

These resources cover topics you may want to explore after the course.

Learning more image analysis

• Introduction to Bioimage Analysis: Pete Bankhead. A free online textbook covering image formation, segmentation and measurement, with worked examples.

• Bio-image Analysis Notebooks by Robert Haase: worked examples of image analysis workflows with Python and scikit-image.

• image.sc forum: a community forum for bioimage analysis questions. Include a representative image, the steps you tried and any relevant code when asking for help.

Further useful tools

• QuPath: for whole-slide and histology images, where the images are far too large for the approach taken here.

• CellProfiler: pipeline-based analysis without writing code. A reasonable middle ground between Fiji and Python for batch work.

• Ilastik: interactive pixel classification, similar in purpose to Trainable Weka Segmentation.

Good Scientific Practice

• Lee, Kitaoka (2018), A beginner’s guide to rigor and reproducibility in fluorescence imaging experiments, Mol. Biol. Cell. What to record, what to report, and what not to adjust.

• Schmied et al. (2024), Community-developed checklists for publishing images and image analyses, Nature Methods. A checklist to run through before submitting a figure.