HistoQC is an open-source quality control tool for digital pathology slides
Tested with Python 3.6
Requires:
- openslide
And the following additional python package:
- python-openslide
- matplotlib
- numpy
- scipy
- skimage
- sklearn
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
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 (In Press)