The code was strongly inspired by the code of : Jun-Yan Zhu and Taesung Park.

• Generate synthetic cell images that model the distribution of the input images for data augmentation. Use both of the synthetic and real cells images for training a contextaware CNN that can accurately segment these cells. See : GANs

• We propose to employ a segmentation method based on cycle-consistent generative adversarial networks that can be trained even in absence of prepared image-mask pairs

• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

• Clone this repo:

git clone https://github.com/AissamDjahnine/CycleGAN.git
cd CycleGAN

• Install PyTorch and 0.4+ and other dependencies (e.g., torchvision and dominate).

  • For pip users, please type the command pip install -r requirements.txt.

• Train a model:

python train.py --dataroot ./datasets/rpedata --name rpedata --model cycle_gan --gan_mode vanilla --dataset_mode unaligned --n_epochs 100 --n_epochs_decay 50 --save_epoch_freq 20 

To see more intermediate results, check out ./checkpoints/maps_cyclegan/web/index.html.

• Test the model:

• Change the --dataroot and --name to be consistent with your trained model's configuration.

• Using --model cycle_gan requires loading and generating results in both directions. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.

• For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model.

python test.py --dataroot datasets/rpedata --name rpedata --dataset_mode aligned --no_dropout  --model cycle_gan --norm

• The test results will be saved to a html file here: ./results/maps_cyclegan/latest_test/index.html.

• For paired mode, please use the python function : combine_A_B.py to join train images. you should have two folders : train and test with joined images.

• For unpaired mode, you should have 4 folders : trainA, trainB, testA, testB with images in both domains.

• You can download an unpaired version of Human U2OS cells : Unpaired_dataset.

• You can download a paired version of Human Retinal Pigment Epithelium (RPE) cells : Paired data

• You find the notebook associated with this projects. after cloning the project , open the notebook and follow instructions

If you're familiar with Tensorboard , skip this section

In your terminal, run:

tensorboard --logdir ./runs

• You should be looking to :

Our code is inspired by pytorch-DCGAN.

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
  Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros. In ICCV 2017.

• "Generative Adversarial Networks." Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. ArXiv 2014.

• "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" Alec Radford, Luke Metz, Soumith Chintala

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please feel free to contact if case of issues.