# Further reading

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

## Learning more image analysis

- **[Introduction to Bioimage Analysis](https://bioimagebook.github.io/)**: Pete
  Bankhead. A free online textbook covering image formation, segmentation and
  measurement, with worked examples.
- **[Bio-image Analysis Notebooks](https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/)**
  by Robert Haase: worked examples of image analysis workflows with Python and
  scikit-image.
- **[image.sc forum](https://forum.image.sc/)**: 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](https://qupath.github.io/)**: for whole-slide and histology images,
  where the images are far too large for the approach taken here.
- **[CellProfiler](https://cellprofiler.org/)**: pipeline-based analysis without
  writing code. A reasonable middle ground between Fiji and Python for batch
  work.
- **[Ilastik](https://www.ilastik.org/)**: 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.