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