stardist 0.9.1

StarDist is a Python package designed for object detection with star-convex shapes, particularly useful in microscopy. It provides implementations for both 2D and 3D images and uses a model trained to predict distances to the object boundary along fixed rays and object probabilities. These predictions produce an overcomplete set of candidate polygons for a given image, with the final result obtained via non-maximum suppression.

StarDist is compatible with Python 3.6 to 3.10, requires TensorFlow, and provides pre-trained models for 2D images and example workflows via Jupyter notebooks, making it a versatile tool for cell detection and segmentation in microscopy.

Stars: 904, Watchers: 904, Forks: 220, Open Issues: 57

The stardist/stardist repo was created 6 years ago and the last code push was 1 weeks ago.
The project is popular with 904 github stars!

How to Install stardist

You can install stardist using pip

pip install stardist

or add it to a project with poetry

poetry add stardist

Package Details

Author

Uwe Schmidt, Martin Weigert

License

BSD-3-Clause

Homepage

https://github.com/stardist/stardist

PyPi:

https://pypi.org/project/stardist/

GitHub Repo:

https://github.com/stardist/stardist

Classifiers

• Scientific/Engineering

Related Packages

Errors

A list of common stardist errors.

Code Examples

Here are some stardist code examples and snippets.

GitHub Issues

The stardist package has 57 open issues on GitHub

• FIJI crashes when trying to process my 32kx32k stitched dapi-stained nuclei TIF
• Running stardist inside imagej, there is a problem importing the trained model
• Fiji crashing upon running trained Stardist model on Mac M1
• Macbook M2 failed to model3d.train with 'GPU'
• Replaced from tqdm import tqdm with from tqdm.auto import tqdm
• Crashes when running predict_instances on a 3D image
• Problem with output segmentation with 2D_versatile_he

See more issues on GitHub