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

Requires:

1. openslide

And the following additional python package:

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

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.

C:\Research\code\qc>python qc_pipeline.py --help
usage: qc_pipeline.py [-h] [-o OUTDIR] [-p BASEPATH] [-c CONFIG] [-f]
                      [-b BATCH] [-n NTHREADS] [-s]
                      [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
  -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 NTHREADS, --nthreads NTHREADS
                        number of threads to launch
  -s, --symlinkoff      turn OFF symlink creation

Prefered usage is to run from the HistoQC directory, .e.g,: HistoQC> python qc_pipeline.py -c config.ini -n 4 remote_file_location/*.svs (Note: filenames in config.ini are relative to directory of execution, unless absolute paths are used)

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.

Afterwards, double click index.html to open front end user interface, select the respective results.tsv file from the Data directory

This can also be done remotely, but is a bit more complex, see advanced usage.

See wiki

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

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