A Deep learning pipeline for segmentation of fluorescent labels in microscopy images
Project description
DeepFLaSH2
Official repository of DeepFLasH2 - a deep learning pipeline for segmentation of fluorescent labels in microscopy images.
Installing
You can use deepflash2 by using Google Colab. You can run every page of the documentation as an interactive notebook - click "Open in Colab" at the top of any page to open it.
- Be sure to change the Colab runtime to "GPU" to have it run fast!
- Use Firefox or Google Chrome if you want to upload your images.
You can install deepflash2 on your own machines with conda (highly recommended):
conda install -c matjesg deepflash2
To install with pip, use
pip install deepflash2
If you install with pip, you should install PyTorch first by following the PyTorch installation instructions.
Using Docker
Docker images for deepflash2 are built on top of the latest pytorch image and fastai images. You must install Nvidia-Docker to enable gpu compatibility with these containers.
- CPU only
docker run -p 8888:8888 matjesg/deepflash
- With GPU support (Nvidia-Docker must be installed.) has an editable install of fastai and fastcore.
docker run --gpus all -p 8888:8888 matjesg/deepflash
All docker containers are configured to start a jupyter server. deepflash2 notebooks are available in thedeepflash2_notebooks
folder.
For more information on how to run docker see docker orientation and setup and fastai docker.
Model Library
We provide a model library with pretrained model weights. Visit our model library documentation for information on the datasets of the pretrained models.
Creating segmentation masks with Fiji/ImageJ
If you don't have labelled training data available, you can use this instruction manual for creating segmentation maps. The ImagJ-Macro is available here.
Acronym
A Deep-learning pipeline for Fluorescent Label Segmentation that learns from Human experts
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