findmycells 0.1.0

An end-to-end bioimage analysis pipeline with state-of-the-art tools for non-coding experts

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 available via both pip and conda:

conda install findmycells

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