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A deep learning based tool to segment epithelial tissues. The epyseg GUI can be uesd to build, train or run custom networks

Project description


EPySeg is a package for segmenting 2D epithelial tissues. EPySeg also ships with a graphical user interface that allows for building, training and running deep learning models. Training can be done with or without data augmentation (2D-xy and 3D-xyz data augmentation are supported). EPySeg relies on the segmentation_models library. EPySeg source code is available here. Cloud version available here.


  1. Install python 3.7 or Anaconda 3.7 (if not already present on your system)

  2. In a command prompt type:

    pip install --user --upgrade epyseg


    pip3 install --user --upgrade epyseg


    • To open a command prompt on Windows press 'Windows'+R then type 'cmd'
    • To open a command prompt on MacOS press 'Command'+Space then type in 'Terminal'
  3. To open the graphical user interface, type the following in a command:

    python -m epyseg


    python3 -m epyseg

Third party libraries

Below is a list of the 3rd party libraries used by EPySeg and/or pyTA.

IMPORTANTLY: if you disagree with any license below, please uninstall EPySeg.

Library name Use Link License
tensorflow Deep learning library Apache 2.0
segmentation-models Models MIT
czifile Reads Zeiss .czi files BSD (BSD-3-Clause)
Markdown Python implementation of Markdown BSD
matplotlib Plots images and graphs PSF
numpy Array/Image computing BSD
Pillow Reads 'basic' images (.bmp, .png, .pnm, ...) HPND
PyQt5 Graphical user interface (GUI) GPL v3
read-lif Reads Leica .lif files GPL v3
scikit-image Image processing BSD (Modified BSD)
scipy Great library to work with numpy arrays BSD
tifffile Reads .tiff files (also reads Zeiss .lsm files) BSD
tqdm Command line progress MIT, MPL 2.0
natsort 'Human' like sorting of strings MIT
numexpr Speeds up image math MIT
urllib3 Model architecture and trained models download MIT
qtawesome Elegant icons in pyTA MIT
pandas Data analysis toolkit BSD (BSD-3-Clause)
numba GPU acceleration of numpy ops BSD
elasticdeform Image deformation (data augmentation) BSD
CARE/csbdeep pyTA uses custom trained derivatives of the CARE surface projection model to generate (denoised) surface projections BSD (BSD-3-Clause)

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