Metadata-Version: 2.1
Name: dynamic-watershed
Version: 1.0.0
Summary:  Post-processing function used in 'Segmentation of 
                  Nuclei in Histopathology Images by deep regression 
                  of the distance map'. 
Home-page: https://github.com/PeterJackNaylor/dynamic_watershed.git
Author: Peter Jack Naylor
Author-email: peter.jack.naylor@gmail.com
License: see LICENSE.txt
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
        --------------
        ```python
        >>> 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}
            }
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
