HistoQC is an open-source quality control tool for digital pathology slides
Tested with Python 3.7 and 3.8 Note: the DockerFile installs Python 3.8, so if your goal is reproducibility you may want to take this into account
And the following additional python package:
- pytest (optional)
You can likely install the python requirements using something like (note python 3+ requirement):
pip3 install -r requirements.txt
The library versions have been pegged to the current validated ones. Later versions are likely to work but may not allow for cross-site/version reproducibility (typically a bad thing in quality control).
Openslide binaries will have to be installed separately as per individual o/s instructions
The most basic docker image can be created with the included (7-line) Dockerfile.
Running the pipeline is now done via a python module:
C:\Research\code\HistoQC>python -m histoqc --help usage: __main__.py [-h] [-o OUTDIR] [-p BASEPATH] [-c CONFIG] [-f] [-b BATCH] [-n NPROCESSES] [--symlink TARGET_DIR] input_pattern [input_pattern ...] positional arguments: input_pattern input filename pattern (try: *.svs or target_path/*.svs ), or tsv file containing list of files to analyze optional arguments: -h, --help show this help message and exit -o OUTDIR, --outdir OUTDIR outputdir, default ./histoqc_output_YYMMDD-hhmmss -p BASEPATH, --basepath BASEPATH base path to add to file names, helps when producing data using existing output file as input -c CONFIG, --config CONFIG config file to use -f, --force force overwriting of existing files -b BATCH, --batch BATCH break results file into subsets of this size -n NPROCESSES, --nprocesses NPROCESSES number of processes to launch --symlink TARGET_DIR create symlink to outdir in TARGET_DIR
HistoQC now has a httpd server which allows for improved result viewing, it can be accessed like so:
C:\Research\code\HistoQC>python -m histoqc.ui --help usage: __main__.py [-h] [--bind ADDRESS] [--port PORT] [--deploy OUT_DIR] [data_directory] positional arguments: data_directory Specify the data directory [default:current directory] optional arguments: -h, --help show this help message and exit --bind ADDRESS, -b ADDRESS Specify alternate bind address [default: all interfaces] --port PORT Specify alternate port [default: 8000] --deploy OUT_DIR Write UI to OUT_DIR
Lastly, supplied configuration files can be viewed and modified like so:
C:\Research\code\HistoQC>python -m histoqc.config --help usage: __main__.py [-h] [--list] [--show NAME] show example config optional arguments: -h, --help show this help message and exit --list list available configs --show NAME show named example config
If you would like, you can install HistoQC into your system by using
git clone https://github.com/choosehappy/HistoQC.git cd HistoQC python -m pip install --upgrade pip # (optional) upgrade pip to newest version pip install -r requirements.txt # install pinned versions of packages pip install .
Installed or simply git-cloned, a typical command line for running the tool thus looks like:
python -m histoqc -c v2.1 -n 3 "*.svs"
which will use 3 process to operate on all svs files using the named configuration file config_v2.1.ini from the config directory.
Alternatively one can specify their own modified config file using an absolute or relative filename:
python -m histoqc.config --show light > mylight.ini python -m histoqc -c ./mylight.ini -n 3 "*.svs"
Afterward completion of slide processing you can view the results in your web-browser simply by following the directions after typing:
python -m histoqc.ui
Which will likely say something like:
HistoQC data directory: 'D:\temp\HistoQC' Serving HistoQC UI on 0.0.0.0 port 8000 (http://0.0.0.0:8000/) ...
Allowing you to browse to http://localhost:8000/ to select your results.tsv file.
In case of errors, HistoQC can be run with the same output directory and will begin where it left off, identifying completed images by the presence of an existing directory.
This can also be done remotely, but is a bit more complex, see advanced usage.
HistoQC's performance is significantly improved if you select an appropriate configuration file as a starting point and modify it to suit your specific use case.
If you would like to see a list of provided config files to start you off, you can type
python -m histoqc.config --list
and then you can select one and write it to file like so for your modification and tuning:
python -m histoqc.config --show ihc > myconfig_ihc.ini
Information from HistoQC users appears below:
- the new Pannoramic 1000 scanner, objective-magnification is given as 20, when a 20x objective lense and a 2x aperture boost is used, i.e. image magnification is actually 40x. While their own CaseViewer somehow determines that a boost exists and ends up with 40x when objective-magnification in Slidedat.ini is at 20, openslide and bioformats give 20x.
1.1. When converted to svs by CaseViewer, the MPP entry in ImageDescription meta-parameter give the average of the x and y mpp. Both values are slightly different for the new P1000 and can be found in meta-parameters of svs as tiff.XResolution and YResolution (inverse values, so have to be converted, also respecting ResolutionUnit as centimeter or inch
If you find this software useful, please drop me a line and/or consider citing it:
"HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides", Janowczyk A., Zuo R., Gilmore H., Feldman M., Madabhushi A., JCO Clinical Cancer Informatics, 2019
Manuscript available here
“Assessment of a computerized quantitative quality control tool for kidney whole slide image biopsies”, Chen Y., Zee J., Smith A., Jayapandian C., Hodgin J., Howell D., Palmer M., Thomas D., Cassol C., Farris A., Perkinson K., Madabhushi A., Barisoni L., Janowczyk A., Journal of Pathology, 2020
Manuscript available here