An end-to-end bioimage analysis pipeline with state-of-the-art tools for non-coding experts
Project description
findmycells
Hi there!
Over the past years, deep-learning-based tools have
become increasingly popular and abundant, particularly in the image
processing domain. In fact, even the image shown next to this text was
created by such a tool - with nothing but a few keywords as input (go
checkout starryai). Similarly,
deep-learning-based image analysis tools also have a growing impact on
biomedical research. However, such deep-learning-powered scientific
software tools are rarely as user-friendly as starryai (or
DeepLabCut,
to name at least one positive exception). And make no mistake, also
findmycells will not be able to make such a giant leap forward.
Instead, it was developed to narrow the gap by bringing state-of-the-art
deep-learning-based bioimage analysis tools to users with little or even
no coding experience. This is achieved, as it integrates them in a full
end-to-end bioimage analysis pipeline that comes with an intuitive and
interactive graphical user interface that runs directly in Jupyter
Notebooks. But enough introduction - please feel free to test it
yourself! Either follow the installation instructions below, or head
over for instance to the
GUI
tutorial to get a first impression!
Installation guide
findmycells is currently only available via pip:
pip install findmycells
Note: Please be aware that findmycells was so far only tested in a Linux subsystem run under Windows (Ubuntu 20.04.5 in WSL2 on both Windows 10 and Windows 11). In addition, having a GPU is highly recommended when using deepflash2 or cellpose for the segmentation of your images.
For developers
This package is developed using nbdev
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