Welcome to the Imaging Server Kit’s documentation!#
The Imaging Server Kit lets you turn Python-based image processing workflows into algorithms that gain extra functionalities.
@sk.algorithm # <- Turn your function into an algorithm
def my_algo(image, parameter):
(...)
Server Kit algorithms are versatile objects that allow you run computations in a variety of ways.
For example, you can
Turn your algorithm into a web server, connect to it and run computations from Napari, QuPath, or Python via HTTP requests.
Generate a dock widget to run your algorithm interactively in Napari or QuPath.
Run your algorithm tile-by-tile on the input image.
Stream results to inspect them in real-time.
On top of that, you can provide samples and automatically generate a documentation page for your algorithm that you can share with users.
This documentation will give you a conceptual overview of the package, and walk you through the steps to learn how to create an algorithm, and give you some suggestions of use cases.
Contents#
Installation#
Install the imaging-server-kit package with pip:
pip install imaging-server-kit
or clone the project and install the development version:
git clone https://github.com/Imaging-Server-Kit/imaging-server-kit.git
cd imaging-server-kit
pip install -e .
To use the Napari-related functionalities, you additionally have to install napari and napari-toolkit which are not included by default. Install the package with:
pip install "imaging-server-kit[napari]"
To use the QuPath-related functionalities, you additionally have to install qubalab>=0.2.0, which is not included by default. Install the package with:
pip install "imaging-server-kit[qupath]"
License#
This software is distributed under the terms of the BSD-3 license.
Citing#
If you use imaging-server-kit in the context of scientific publication, you can cite it as below.
BibTeX:
@software{mallory_wittwer_2025_15673152,
author = {Mallory Wittwer and Edward Andò and Maud Barthélemy and Florian Aymanns},
title = {Imaging-Server-Kit/imaging-server-kit: v0.0.14},
url = {https://doi.org/10.5281/zenodo.15673152},
doi = {10.5281/zenodo.15673152},
version = {v0.0.14},
year = 2025,
}
Acknowledgements#
We thank the Personalized Health and Related Technologies for funding this project.