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!
findmycells is currently only available via pip. findmycells was so far tested in Windows 7, MacOS Ventura and in a Linux subsystem run under Windows (Ubuntu 20.04.5 in WSL2 on both Windows 10 and Windows 11). Moreover, having a CUDA-capable GPU is highly recommended when using deepflash2 or cellpose for the segmentation of your images. You can run findmycells on Google Colab as well. For local installation, please follow the steps below:
We highly recommend that you use Anaconda. In your terminal (command line, or Anaconda Prompt), run the following line to create a new conda environment, called “findmycells”:
conda create -n findmycells
Once the environment is created, activate it as suggested by conda by running:
conda activate findmycells
You should now see (findmycells) at the beginning of the line.
Continue by installing pip:
conda install pip
Once the installations are done, all you need to do is run:
pip install findmycells
And all required packages should come with it.
If you would like to use your GPU (again: using a GPU is highly recommended, if you want to use the integrated segmentation tools deepflash2 and cellpose), you can test whether it is accessible for python by running the following commands again in the terminal (with the findmycells environment still activated):
python
This will now launch a python interface right in your terminal. Now continue with importing torch:
import torch
As soon as torch is imported, you can check for GPU accessability by running:
torch.cuda.is_available()
This will now output either True (whoop whoop - everything is
working - well done!) or False ( :( ). In case you have a local
CUDA-capable GPU installed & you still got a False, confirm that you
have the latest GPU driver installed, and that you are using the latest
available Windows, WSL2, and Linux builds. If you’re still not able to
access the GPU, it may sometimes help to start by installing pytorch
first (mix & match the correct versions for you
here), and to first confirm
that the GPU is indeed accessible. If it is, you can now continue with
the installation of findmycells & hope that nothing breaks! Fingers
crossed, that you can find a version that satisfies your local
requirements & also those of deepflash2 & cellpose!
This package is developed using nbdev