A Python toolkit for Histopathology Image Analysis
Project description
HistomicsTK is a Python toolkit 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.
You may view the following introductory videos for more information about DSA and HistomicsTK:
General overview: https://www.youtube.com/watch?v=NenUKZaT–k
Simple annotation and data management tutorial: https://www.youtube.com/watch?v=HTvLMyKYyGs
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 image-processing task library for HistomicsUI and the Digital Slide Archive: This is intended to allow pathologists/biologists to apply analysis modules/pipelines containerized in HistomicsTK’s docker plugins on data over the web. See the Digital Slide Archive for installation instructions.
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.
Previous Versions
This repository used to contain almost all of the Digital Slide Archive and HistomicsUI. The deployment and installation code and instructions have moved to the Digital Slide Archive repository. The user interface and annotation drawing code has moved to the HistomicsUI repository.
This repository contains image-processing algorithms and tasks.
Funding
This work is funded by the NIH grant U24-CA194362-01.
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