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Post-processing function used in 'Segmentation of Nuclei in Histopathology Images by deep regression of the distance map'.

Project description

dynamic_watershed

Package description

We implement the splitting algorithm for splitting nuclei nucleas described in in 'Nuclei segmentation in histopathology images using deep neural networks'. This algorithm is essentially a dynamic watershed. The main function is named: post_process.

Installation

dynamic_watershed can be installed by unzipping the source code in one directory and using this command: ::

python setup.py install

You can also install it directly from the Python Package Index with this command (not working yet): ::

pip install dynamic_watershed

Example

>>> from dynamic_watershed import post_process
>>> from skimage.io import imread
>>> probability_image = imread('example.png')
>>> p1, p2 = 7, 0.5
>>> result_segmentation = post_process(probability_image, p1, thresh=p2)

Licence

See file LICENCE.txt in this folder.

Contribute

dynamic_watershed is an open-source software. Everyone is welcome to contribute !

Cite

If you use this work please cite our paper.

BibTeX:

  @inproceedings{naylor2017nuclei,
    title={Nuclei segmentation in histopathology images using deep neural networks},
    author={Naylor, Peter and La{\'e}, Marick and Reyal, Fabien and Walter, Thomas},
    booktitle={Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on},
    pages={933--936},
    year={2017},
    organization={IEEE}
    }

Project details


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Files for dynamic-watershed, version 1.1.3
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Filename, size dynamic_watershed-1.1.3.tar.gz (5.3 kB) File type Source Python version None Upload date Hashes View

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