Skip to main content

A Python toolkit for Histopathology Image Analysis

Project description

HistomicsTK is a Python and REST API for the analysis of Histopathology images in association with clinical and genomic data.

Histopathology, which involves the examination of thin-slices of diseased tissue at a cellular resolution using a microscope, is regarded as the gold standard in clinical diagnosis, staging, and prognosis of several diseases including most types of cancer. The recent emergence and increased clinical adoption of whole-slide imaging systems that capture large digital images of an entire tissue section at a high magnification, has resulted in an explosion of data. Compared to the related areas of radiology and genomics, there is a dearth of mature open-source tools for the management, visualization and quantitative analysis of the massive and rapidly growing collections of data in the domain of digital pathology. This is precisely the gap that we aim to fill with the development of HistomicsTK.

Developed in coordination with the Digital Slide Archive and large_image, HistomicsTK aims to serve the needs of both pathologists/biologists interested in using state-of-the-art algorithms to analyze their data, and algorithm researchers interested in developing new/improved algorithms and disseminate them for wider use by the community.

HistomicsTK can be used in two ways:

  • As a pure Python package: This is intended to enable algorithm researchers to use and/or extend the analytics functionality within HistomicsTK in Python. HistomicsTK provides algorithms for fundamental image analysis tasks such as color normalization, color deconvolution, cell-nuclei segmentation, and feature extraction. Please see the api-docs and examples for more information.

    Installation instructions on Linux:

    To install HistomicsTK using PyPI:

    $ python -m pip install histomicstk

    To install HistomicsTK from source:

    $ git clone https://github.com/DigitalSlideArchive/HistomicsTK/
    $ cd HistomicsTK/
    $ python -m pip install setuptools-scm Cython>=1.25.2 scikit-build>=0.8.1 cmake>=0.6.0 numpy>=1.12.1
    $ python -m pip install -e .

    HistomicsTK uses the large_image library to read and various microscopy image formats. Depending on your exact system, installing the necessary libraries to support these formats can be complex. There are some non-official prebuilt libraries available for Linux that can be included as part of the installation by specifying pip install histomicstk --find-links https://girder.github.io/large_image_wheels. Note that if you previously installed HistomicsTK or large_image without these, you may need to add --force-reinstall --no-cache-dir to the pip install command to force it to use the find-links option.

    The system version of various libraries are used if the --find-links option is not specified. You will need to use your package manager to install appropriate libraries (on Ubuntu, for instance, you’ll need libopenslide-dev and libtiff-dev).

  • As a server-side Girder plugin for web-based analysis: This is intended to allow pathologists/biologists to apply analysis modules/pipelines containerized in HistomicsTK’s docker plugins on data over the web. Girder is a Python-based framework (under active development by Kitware) for building web-applications that store, aggregate, and process scientific data. It is built on CherryPy and provides functionality for authentication, access control, customizable metadata association, easy upload/download of data, an abstraction layer that exposes data stored on multiple backends (e.g. Native file system, Amazon S3, MongoDB GridFS) through a uniform RESTful API, and most importantly an extensible plugin framework for building server-side analytics apps. To inherit all these capabilities, HistomicsTK is being developed to act also as a Girder plugin in addition to its use as a pure Python package. To further support web-based analysis, HistomicsTK depends on three other Girder plugins: (i) girder_worker for distributed task execution and monitoring, (ii) large_image for displaying, serving, and reading large multi-resolution images produced by whole-slide imaging systems, and (iii) slicer_cli_web to provide web-based RESTFul access to image analysis pipelines developed as slicer execution model CLIs and containerized using Docker.

Please refer to https://digitalslidearchive.github.io/HistomicsTK/ for more information.

For questions, comments, or to get in touch with the maintainers, head to our Discourse forum, or use our Gitter Chatroom.

This work is funded by the NIH grant U24-CA194362-01.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

histomicstk-0.1.5.dev37-cp37-cp37m-manylinux1_x86_64.whl (944.2 kB view details)

Uploaded CPython 3.7m

histomicstk-0.1.5.dev37-cp36-cp36m-manylinux1_x86_64.whl (944.3 kB view details)

Uploaded CPython 3.6m

histomicstk-0.1.5.dev37-cp35-cp35m-manylinux1_x86_64.whl (939.4 kB view details)

Uploaded CPython 3.5m

histomicstk-0.1.5.dev37-cp27-cp27mu-manylinux1_x86_64.whl (955.3 kB view details)

Uploaded CPython 2.7mu

File details

Details for the file histomicstk-0.1.5.dev37-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: histomicstk-0.1.5.dev37-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 944.2 kB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.37.0 CPython/3.6.9

File hashes

Hashes for histomicstk-0.1.5.dev37-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 8f3c83c3524aff3998e5a1f47c00f8cd2c2b688fad8a8969e4b797f7805b900b
MD5 7b73cbe6fb1f3750fffd9f0fe9dc5e32
BLAKE2b-256 243657c2fda3bda23e8e0ffc92987cb751612dc425be761eac1f978883e92521

See more details on using hashes here.

Provenance

File details

Details for the file histomicstk-0.1.5.dev37-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: histomicstk-0.1.5.dev37-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 944.3 kB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.37.0 CPython/3.6.9

File hashes

Hashes for histomicstk-0.1.5.dev37-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 dfe418b5d8e01eed39092793d8c9dc015976f139d45f7137c643a755486696f8
MD5 b0ebcc6f6137d2a322b87fa0aebbc471
BLAKE2b-256 3e6a1ea0d6dae8c4df294aefc180aead53e892e34ec9fa3c2ad9e1e985333a90

See more details on using hashes here.

Provenance

File details

Details for the file histomicstk-0.1.5.dev37-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: histomicstk-0.1.5.dev37-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 939.4 kB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.37.0 CPython/3.6.9

File hashes

Hashes for histomicstk-0.1.5.dev37-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 d2bfbc536aa69803b9cd1dcdecc5b5c84cfbc740c15eb830d880227d99b1696f
MD5 19a5b2484745d6fe1508c9b4d4bdb6a5
BLAKE2b-256 fca7ca78e2904f1d9efa7b5e9ecbc2d21d383aaad8f4dc17e83a40089174e43c

See more details on using hashes here.

Provenance

File details

Details for the file histomicstk-0.1.5.dev37-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: histomicstk-0.1.5.dev37-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 955.3 kB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.37.0 CPython/3.6.9

File hashes

Hashes for histomicstk-0.1.5.dev37-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a7b11708abc5693b4adc4ca3fdcaad11f793f1f3e17f7403b04d67205ac9290f
MD5 b56bf21b8a63e7d9342f68efc63b9a59
BLAKE2b-256 991a793bff4e10a224cf22688a47e7aba656b87b90721c46a024bbb3c12e234e

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page