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All necessary Python packages are in requirements-pipeline.txt. This pipeline --- Haemorasis --- was developed and tested using Python 3.6.8 and on 8GB RAM + 12GB VRAM on CentOS Linux 8 (kernel: Linux 4.18.0-240.22.1.el8_3.x86_64). Haemorasis is composed of 7 steps (0 is optional) and is orchestrated by a snakemake pipeline (Snakefile):

0. The model checkpoints are downloaded if they are not available
1. Quality control of the PBS --- each 512*512 tile is quality controlled to filter out tiles with excessive/defficient cellular density and/or poor resolution (output stored as {output.directory}/_quality_control/{slide.id}.h5)
2. Segmentation of WBC and RBC --- segmentation coordinates are stored in hdf5 format, with a dataset for each cell (output stored as {output.directory}/_segmented_wbc/{slide.id}.h5 and {output.directory}/_segmented_rbc/{slide.id}.h5)
3. Morphometric characterization of WBC --- morphometric features are stored in hdf5 format, with a dataset for each cell (output stored as {output.directory}/_aggregates_wbc/{slide.id}.h5)
4. Morphometric characterization of RBC --- morphometric features are stored in hdf5 format, with a dataset for each cell (output stored as {output.directory}/_aggregates_rbc/{slide.id}.h5)
5. Annotation of WBC in geojson format --- these annotations can be loaded into QuPath (output stored as {output.directory}/_annotations_wbc/{slide.id}.h5)
6. Annotation of RBC in geojson format --- these annotations can be loaded into QuPath (output stored as {output.directory}/_annotations_rbc/{slide.id}.h5)

Steps 3 and 4 are run in parallel, as well as steps 5 and 6. In the examples above, {slide.id} is the basename of the slide until the first point (if the slide path is /homes/user/slide_32.0.1.tiff then {slide.id} is slide_32). The output directory is specified in the scripts below as -o.

To test the code provided here, you must first have a working Python 3.6.8 version, at least 8GB of RAM and a GPU card with at least 12GB of VRAM and CUDA capabilities. This has been tested on operating systems based on either Ubuntu or CentOS. Then:

1. Install the packages listed in requirements-pipeline.txt
2. Download a peripheral blood slide from https://www.ebi.ac.uk/biostudies/studies/S-BIAD440, a BioImage Archive dataset containing all slides used in this work
3. Run the Haemorasis described below ("Usage").

A Docker container has been made available to facilitate the application of this pipeline in https://hub.docker.com/repository/docker/josegcpa/blood-cell-detection. Instructions on Docker usage are provided in the Docker container landing page and in the Supplementary Materials of the publication where this software was used (available on Nature Communications) under "Setting up Haemorasis" and "Running Haemorasis".

arguments (required):
                -i : input slide
                -o : output directory

arguments (optional):
                -q : path to quality control network checkpoint (if not in -q will be downloaded)
                -u : path to WBC segmentation network checkpoint (if not in -u will be downloaded)
                -x : path to RBC object filtering XGboost model parameters (if not in -u will be downloaded)
                -r : rescale factor (default = 1.0)
                -m : mode (local or cluster (only LSF supported))

example:
                bash run-slide.sh -i slide_dir -o output_dir -f tiff -r 1.1

An example of running Haemorasis is, for a slide /homes/user/slides/slide_a.tiff and the output directory in /homes/user/output is sh run-slide.sh -i /homes/user/slides/slide_a.tiff -o /homes/user/output. sh run-slide.sh -h displays other available options.

arguments (required):
                -i : input directory
                -f : file extension (i.e. ndpi, svs, tiff, etc...)
                -o : output directory

arguments (optional):
                -q : path to quality control network checkpoint (if not in -q will be downloaded)
                -u : path to WBC segmentation network checkpoint (if not in -u will be downloaded)
                -x : path to RBC object filtering XGboost model parameters (if not in -u will be downloaded)
                -r : rescale factor (default = 1.0)
                -m : mode (local or cluster (only LSF supported))

example:
                bash run-folder.sh -i slide_dir -o output_dir -f tiff -r 1.1