preProcessing

This repository contains two main scripts for the preProcessing of the Whole Slide Images (WSIs) as an initial step for histopathological deep learning.

0. Install openslide obn Fedora by dnf install openslide-tools Set up python environment with pip install -r requirements.txt

1. extractTiles-ws : This script is used to tessellate the WSIs. The main required inputs for this function:

Input Variable name	Description
-s	Path to the WSI folder
-o	Path to the output folder, to save the tiles
--skipws	To skip the tessellation of WSI if annotation is missing. Default value is False.
-px	Size of image patches to analyze, in pixels
-um	Size of image patches to analyze, in microns.
--num_threads	Number of threads to use when tessellating.
--augment	Augment extracted tiles with flipping/rotating.
--ov	The Size of overlappig for extracted tiles. It can be values between 0 and 1.

2. Normalize : This script is used to normalize the extracted tiles using Macenko method. The main required inputs for this function:

Input Variable name	Description
-inputPath	Path to the BLOCKS folder, where the tiles are saved
-outputPath	Path to the output folder, to save the normalized tiles
--sampleImagePath	Path to one sample tile, which it's color distribution will used as a template for all the tiles.

In this script, we are using the Macenko normalization method from https://github.com/wanghao14/Stain_Normalization.git repository.