The scikit-image team strives to cater to an ever larger number of scientists, whether students or senior investigators, whether beginners or experienced practitioners of image processing. Life sciences are of particular importance to us. Indeed, biological research keeps growing and branching out, it is heavily funded, and it brings into play rich datasets (2 or 3 spatial dimensions, time dimension, multiple colour channels, various colour models).
In consequence, we have been meaning to expand the scikit-image gallery with examples pertaining to biological applications and/or featuring biomedical images. As of today, interested users are invited to consult the following tutorials:
- Segment human cells (in mitosis) — skimage v0.19.0.dev0 docs
- Trainable segmentation using local features and random forests — skimage v0.19.0.dev0 docs
- Apply maskSLIC vs SLIC — skimage v0.19.0.dev0 docs
- Separate colors in immunohistochemical staining — skimage v0.19.0.dev0 docs
- Explore 3D images (of cells) — skimage v0.19.0.dev0 docs
- Estimate anisotropy in a 3D microscopy image — skimage v0.19.0.dev0 docs
With this post, we want to reach out to the broader life science communities. We would like to collaborate on new examples which would reflect the practices, needs, and challenges faced by life scientists when analyzing image data. We expect that these interactions would lead to emulation, pushing us to implement, say, missing filters or options, while providing the collaborating scientist with more performant and/or more elegant image processing strategies.
In practice, we would be happy to write up short, self-contained tutorials based on your real-world analyses of microscopy, ultrasound, or CT scan images. We would of course credit you and cite you, if applicable.
Naturally, feedback on existing materials would be most welcome as well. Let us mention that we also curate a repository of datasets, where additional images obtained from biological and medical imaging would be greatly appreciated. Please contribute images under license CC0 or in the public domain. We are also interested in collaborating on demos involving larger cloud-based datasets that would not be suitable for running in real time as part of our documentation build process. These larger-scale demos would illustrate solutions to real-world challenges in working with large data, but we would not host the datasets for these in our repository.
We look forward to hearing from you! Don’t hesitate to reply to this post with your example idea. Alternatively, you can email us at firstname.lastname@example.org or create an issue at our GitHub repository.
Thank you for your attention!
Marianne, for the scikit-image core team