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HistoQC


HistoQC is an open-source quality control tool for digital pathology slides

screenshot

Requirements


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

Requires:

  1. openslide

And the following additional python package:

  1. python-openslide
  2. matplotlib
  3. numpy
  4. scipy
  5. skimage
  6. sklearn
  7. 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.

Basic Usage


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.

Configuration modifications


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

Advanced Usage


See wiki

Notes

Information from HistoQC users appears below:

  1. 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

Citation


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