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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
for more information.
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.
This work is funded by the NIH grant U24-CA194362-01_.
Please refer to https://digitalslidearchive.github.io/HistomicsTK/ for more information.
.. _Digital Slide Archive: http://github.com/DigitalSlideArchive
.. _Docker: https://www.docker.com/
.. _Kitware: http://www.kitware.com/
.. _U24-CA194362-01: http://grantome.com/grant/NIH/U24-CA194362-01
.. _CherryPy: http://www.cherrypy.org/
.. _Girder: http://girder.readthedocs.io/en/latest/
.. _girder_worker: http://girder-worker.readthedocs.io/en/latest/
.. _large_image: https://github.com/girder/large_image
.. _slicer_cli_web: https://github.com/girder/slicer_cli_web
.. _slicer execution model: https://www.slicer.org/slicerWiki/index.php/Slicer3:Execution_Model_Documentation