Introduction

next-preprocesshisto is a best-practice histopathology analysis pipeline designed to process and extract patches from H&E slides provided as input. The slides will go through a RGB thresholding and edge detection to isolate the tissue before being tessellated into small patches. Subsequently, these patches will be normalized for downstream analysis.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.

Pipeline summary

• Input: Read folder with medical images in svs format.
• Processing: The first module utilize RGB thresholding and Canny edge detection to isolate the tissue and then to extract patches for further analysis. Furthermore, it performs a check, discarding patches that are almost completely white, i.e., that have a minimal region of tissue, therefore negligible. The second module deals with the normalization of the extracted patches. It is carried out through the Macenko method.
• Output: Tissue-relevant patches are saved in folder patches. Optionally, the pipeline saves the patches not interested by the tissue, as well as the image reconstruction, in folders discard and reconstruction, respectively. On the other hand, normalized patches are saved in the folder normalized_patches.

Quick Start

1. Install Nextflow (>=22.10.1)

2. Download the pipeline and clone the repository

   git clone [email protected]:sandrocarollo/next-preprocesshisto.git

3. Create and activate conda environment

   conda env create -f environment.yml
   conda activate histopreprocess

4. Run the pipeline

   nextflow run main.nf 

5. Start running your own analysis!

   Use:

   nextflow run main.nf --input "path/to/data/folder" --canny_values 40 100 --threshold_cutoff 0.9 --patch_size 512 --full_saving false --outdir './results'

   where in the flag --input replace the predefined folder structure containing the svs files.

   Optionally, you can add the following flags to set your own parameters and adapt the processing to your needs: --canny_values to set the two limit values for edge detection [ default values: 40 100 ] --threshold_cutoff to set the threshold value above which the patch is discarded because it is considered almost completely blank [ default value: 0.9 ] --patch_size to set the size of patches [ default value: 512 ] --full_saving to choose whether to obtain only the patches interested by the tissue (false) or also the discarded patches and image reconstruction (true) [ default value: false ] --outdir to set the folder where the pipeline output is to be obtained [ default value: './results' ]

Acknowledgments

next/preprocesshisto was originally written by Matteo Girotto, Sandro Carollo.