pypi i epyseg



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 nameUseLinkLicense
tensorflowDeep learning libraryhttps://pypi.org/project/tensorflow/Apache 2.0
czifileReads Zeiss .czi fileshttps://pypi.org/project/czifile/BSD (BSD-3-Clause)
MarkdownPython implementation of Markdownhttps://pypi.org/project/Markdown/BSD
matplotlibPlots images and graphshttps://pypi.org/project/matplotlib/PSF
numpyArray/Image computinghttps://pypi.org/project/numpy/BSD
numpydocNumpy documentation formathttps://pypi.org/project/numpydoc/BSD
PillowReads 'basic' images (.bmp, .png, .pnm, ...)https://pypi.org/project/Pillow/HPND
PyQt5Graphical user interface (GUI)https://pypi.org/project/PyQt5/GPL v3
PyQtWebEngineDisplay html in GUIhttps://pypi.org/project/PyQtWebEngine/GPL v3
read-lifReads Leica .lif fileshttps://pypi.org/project/read-lif/GPL v3
scikit-imageImage processinghttps://pypi.org/project/scikit-image/BSD (Modified BSD)
scipyGreat library to work with numpy arrayshttps://pypi.org/project/scipy/BSD
tifffileReads .tiff files (also reads Zeiss .lsm files)https://pypi.org/project/tifffile/BSD
tqdmCommand line progresshttps://pypi.org/project/tqdm/MIT, MPL 2.0
natsort'Human' like sorting of stringshttps://pypi.org/project/natsort/MIT
numexprSpeeds up image mathhttps://pypi.org/project/numexpr/MIT
urllib3Model architecture and trained models downloadhttps://pypi.org/project/urllib3/MIT
qtawesomeElegant icons in pyTAhttps://pypi.org/project/QtAwesome/MIT
pandasData analysis toolkithttps://pypi.org/project/pandas/BSD (BSD-3-Clause)
numbaGPU acceleration of numpy opshttps://pypi.org/project/numba/BSD
elasticdeformImage deformation (data augmentation)https://pypi.org/project/elasticdeform/BSD
CARE/csbdeeppyTA uses custom trained derivatives of the CARE surface projection model to generate (denoised) surface projectionshttps://pypi.org/project/csbdeep/BSD (BSD-3-Clause)

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