Metadata-Version: 1.0
Name: py_wsi
Version: 1.1
Summary: Python package for dealing with whole slide images (.svs) for machine learning, including
                intuitive, painless patch sampling using OpenSlide, automatic labeling from ImageScope XML 
                annotation files, and functions for saving these patches and their meta data into lightning
                memory-mapped databases (LMDB) for quick reads.
            
Home-page: https://github.com/ysbecca/py-wsi
Author: Rebecca Stone
Author-email: ysbecca@gmail.com
License: GNU General Public License v3.0
Description: py-wsi
        ======
        
        Current version
        ---------------
        
        **Notice: it is strongly recommended to use py-wsi version >= 1.0.**
        
        The current update to py_wsi has added three major improvements which
        are essential for dealing with very large datasets of .svs images:
        
        -  better memory management
        -  error handling
        -  functionality to allow for sampling test patches before sampling from
           all images
        
        See this blog post `py_wsi for computer analysis on whole slide .svs
        images using OpenSlide <https://ysbecca.github.io>`__ for help on
        understanding the relationship between patch and tile sampling. The test
        patch sampling functionality in this version will also help users to
        know exactly what they are sampling.
        
        For any early users who have downloaded previous versions of py_wsi (<
        1.0) I would **strongly suggest downloading the update**. Please feel
        free to submit any issues to the GitHub repository and I will provide
        help as I am able to.
        
        While suggestions for extra/additional functionality will not be
        immediately considered, pull requests are welcome.
        
        Introduction to py_wsi
        ----------------------
        
        py-wsi provides a series of Python classes and functions which deal with
        databases of whole slide images (WSI), or Aperio .svs files for machine
        learning, using Python OpenSlide. py-wsi provides functions to perform
        patch sampling from .svs files, generation of metadata, and several
        store options: saving to a lightning memory-mapped database (LMDB), HDF5
        files, or disk.
        
        These Python functions deal with whole slide images (WSI), or Aperio
        .svs files for deep learning, using OpenSlide. py-wsi provides functions
        to perform patch sampling from .svs files, generation of metadata, and
        several store options: saving to a lightning memory-mapped database
        (LMDB), HDF5 files, or disk.
        
        Lim et al. in “`An analysis of image storage systems for scalable
        training of deep neural
        networks <http://www.bafst.com/events/asplos16/bpoe7/wp-content/uploads/analysis-image-storage.pdf>`__”
        perform a thorough evaluation of the best image storage systems, taking
        into consideration memory usage and access speed. LMDB, a B+tree based
        key-value storage, is not the most memory efficient, but provides
        optimal read time.
        
        py-wsi uses OpenSlide Python. According to the `Python OpenSlide
        website <http://openslide.org/api/python/>`__, “OpenSlide is a C library
        that provides a simple interface for reading whole-slide images, also
        known as virtual slides, which are high-resolution images used in
        digital pathology. These images can occupy tens of gigabytes when
        uncompressed, and so cannot be easily read using standard tools or
        libraries, which are designed for images that can be comfortably
        uncompressed into RAM. Whole-slide images are typically
        multi-resolution; OpenSlide allows reading a small amount of image data
        at the resolution closest to a desired zoom level.”
        
        *Note: HDF5 functionality will not be available until version 1.2*
        
        **Check Jupyter Notebook on GitHub to view example usage:**\ `Example
        usage of
        py-wsi <https://github.com/ysbecca/py-wsi/blob/master/Using%20py-wsi.ipynb>`__
        
        Setup
        -----
        
        This library is dependent on the following, but may be compatible with
        previous versions.
        
        python 3.6.1 numpy 1.12.1 openslide-python 1.1.1
        
        1. Check dependencies listed in setup.py; notably, openslide-python
           which requires openslide, and lmdb. The python geometry package
           Shapely is used for inferring labels from XML annotations.
        
        ::
        
            brew install openslide
        
        2. Install py_wsi using pip.
        
        ::
        
            pip install py_wsi
        
        3. Check out Jupyter Notebook “Using py-wsi” to see what py-wsi can do
           and get started!
        
        **Feel free to contact me with any issues and feedback.**
        
Keywords: whole slide images svs openslide lmdb machine learning
Platform: UNKNOWN
