Second-harmonic Generation Collagen Image Synthesis from Hematoxylin and Eosin Image Using Image-to-image Translation Neural Network

Program for a complete H&E-SHG synthesizing workflow

Input H&E	Synthesized Collagen Image

Required packages

Install required packages in a virtual environment, commands for anaconda/miniconda are listed

• python==3.6.x

  conda create --name [NAME_ENV] python=3.6
  conda activate [NAME_ENV]

• matplotlib==3.1.2

  conda install -c conda-forge matplotlib=3.1.2

• numpy==1.17.4

  conda install -c anaconda numpy=1.17.4

• pandas==0.25.3

  conda install -c anaconda pandas=0.25.3

• Pillow==5.3.0

  conda install -c anaconda pillow=5.3.0

• pyimagej==0.4.0

conda install -c conda-forge pyimagej

• scikit-image==0.16.2

  conda install -c anaconda scikit-image=0.16.2

• tqdm==4.42.0

  conda install -c conda-forge tqdm=4.42.0

• pytorch>=1.3.1

  conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

Download example testing data, trained model weights, FIJI

Execute download.py

python download.py

Run demo

Execute main.py

python main.py

Output images are saved in "output_test_default" folder by default.

Argumenets for main.py

[--use-cuda]          # 1: use GPU, 0: use CPU                            default: (int) 1
[--which-gpu]         # index of the GPU                                  default: (int) 0
[--input-folder]      # name of input folder (input_test_[NAME])          default: (str) default
[--intensity]         # output intensity rescale                          default: (tuple) (20, 180)
[--pilot]             # 1: process the first image, 0: process all images default: (int) 0

Test customized images:

1. Create a folder named "input_test_[NAME]" containing input images.
2. Execute main.py with option "--input-folder=[NAME]".

python main.py --input-folder=[NAME]

3. Output images are saved in "output_test_[NAME]" folder.