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detCycleGAN_pytorch

This repo contains the reference implementation of detcyclegan in Pytorch, for the paper

Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation

Lalith Sharan, Gabriele Romano, Sven Koehler, Halvar Kelm, Matthias Karck, Raffaele De Simone, Sandy Engelhardt

Accepted, IEEE JBHI 2021

Please see the license file for terms os use of this repo. If you find our work useful in your research please consider citing our paper:

@article{sharan_mutually_2021,
	title = {Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation},
	issn = {2168-2208},
	doi = {10.1109/JBHI.2021.3099858},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	author = {Sharan, Lalith and Romano, Gabriele and Koehler, Sven and Kelm, Halvar and Karck, Matthias and De Simone,
	Raffaele and Engelhardt, Sandy},
	year = {2021},
	note = {Conference Name: IEEE Journal of Biomedical and Health Informatics},
	keywords = {CycleGAN, Generative adversarial networks, Generative Adversarial Networks, Landmark Detection, Landmark
	Localization, Maintenance engineering, Mitral Valve Repair, Semantics, Surgery, Surgical Simulation, Surgical
	Training, Task analysis, Training, Valves},
	pages = {1--1},
}

Setup

A conda environment is recommended for setting up an environment for model training and prediction. There are two ways this environment can be set up:

  1. Cloning conda environment (recommended)
conda env create -f detcyclegan.yml
conda activate detcyclegan
  1. Installing requirements
conda intall --file conda_requirements.txt
conda install -c pytorch torchvision=0.7.0
pip install --r requirements.txt

Prediction of suture detection for a single image

You can predict depth for a single image with:

python test.py --dataroot ~/data/mkr_dataset/ --exp_dir ~/experiments/unet_baseline_fold_1/ --save_pred_points
  • The command save_pred_points saves the predicted landmark co-ordinates in the resepective op folders in the ../predictions directory.
  • The command save_pred_mask saves the predicted mask that is the output of the model in the resepective op folders in the ../predictions directory. The final points are extracted from this mask.

Dataset preparation

You can download the challenge dataset from the synapse platform by signing up for the AdaptOR 2021 Challenge from the Synapse platform.

  • The Challenge data is present in this format: dataroot --> op_date --> video_folders --> images, point_labels
  • Generate the masks with a Gaussian likelihood by running the following script: You can predict depth for a single image with:
python generate_masks.py --dataroot /path/to/data
  • Generate the split files for the generated masks, for cross-validation by running the following script: You can predict depth for a single image with:
python generate_splits.py --splits_name mkr_dataset --num_folds 4