Code, dataset, and trained models for "EndoL2H: Deep Super-Resolution for Capsule Endoscopy"
If you use this code, please cite:
Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce, Kagan Incetan, Guliz Irem Gokceler, Muhammed Ali Simsek, Kivanc Ararat, Richard J. Chen, Nichalos J. Durr, Faisal Mahmood, Mehmet Turan. "EndoL2H: Deep Super-Resolution for Capsule Endoscopy." arXiv:2002.05459 (2020).
We propose and quantitatively validate a novel framework to learn a mapping from low-to-high resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8x, 10x, 12x, respectively. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization.
Our main contributions are as follows:
- Spatial Attention-based Super Resolution cGAN: We propose a spatial attention based super-resolution cGAN architecture specifically designed and optimized for capsule endoscopy images.
- High fidelity loss function: We introduce EndoL2H loss which is a weighted hybrid loss function specifically optimized for endoscopic images. It collaboratively combines the strengths of perceptual, content, texture, and pixel-based loss descriptions and improves image quality in terms of pixel values, content, and texture. This combination leads to the maintenance of the image quality even under high scaling factors up to, 10x-12x.
- Qualitative and quantitative study: We conduct a detailed quantitative analysis to assess the effectiveness of our proposed approach and compare it to alternative state-of-the art approaches.
A conditional GAN combined with spatial attention unit maps low resolution(LR) endoscopic images to diagnostically relevant high resolution(HR) endoscopic images. Unlike an unconditional GAN, both the generator and discriminator observe the input LR images.
a) Overall system architecture of EndoL2H super-resolution framework. A low resolution input image is fed to the generator that creates an estimated high resolution counterpart, which is then served to the discriminator. The Markovian discriminator takes tuples of an LR input image and the corresponding HR image (real or generated), and tries to recognize whether the HR image is real or fake. Our generator is U-net with additional SAB layer which is sequentially downsampling tensor by factor of 2 until the latent feature representation and upsampled by the following decoder layers by a factor of 2. We are using convolutional PatchGAN as a classifier which penalize the structure in accordance with the image patch sizes (30x30). b) is the flow diagram of the spatial attention block (SAB) which is selectively focus on clinically more relevant regions and also its output images are presented. c) The feature maps of attention U-Net which is the summary of applied filters and their input-output tensor sizes for 8x.The low resolution images are of 128x128 sizes and their size changes before and after each convolution layers are given. As seen, SAB block preserves the tensor sizes. Finally, the tensor size ends up with 1024x1024 for 8x.
- Clone this repo:
cd ~
git clone https://github.com/akgokce/EndoL2H
cd EndoL2H-
Linux (Tested on Ubuntu 16.04)
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NVIDIA GPU (Tested on Nvidia P100 using Google Cloud)
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CUDA, CuDNN
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Python 3
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Pytorch>=0.4.0
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torchvision>=0.2.1
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dominate>=2.3.1
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visdom>=0.1.8.3
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scipy
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Install PyTorch and 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
- For pip users, please type the command
pip install -r requirements.txt. - For Conda users, you can use an installation script
./scripts/conda_deps.sh. Alternatively, you can create a new Conda environment usingconda env create -f environment.yml.
- For pip users, please type the command
The code base structure is explained below:
- train.py: Script for image-to-image translation. It works for different models (with option '--model': e.g. pipx2pix, cyclegan, colorization) and various datasets (with option '--dataset_mode': e.g. aligned, unaligned, single, colorization). It creates model, dataset and visualizer given the option. Then, it does training. Use '--continue_train' and '--epoch_count' to resume your previous training.
- test.py: You can use this script to test the model after training. First, it creates model and dataset given the option. Then, it runs interference for --num_test images and save results to an HTML file. Use '--results_dir' to specify the results directory.
- networks.py: It contains PyTorch model definitions for all network.
- base_options.py: It defines options used during both training and test time.
- psnr_ssim_calculations.ipynb: You can use this script to see overall and fold by fold PSNR and SSIM results.
- combine_A_and_B.py: pix2pix training requires paired data. It generates training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene. Corresponding images in a pair {A,B} must be the same size and have the same filename. Once the data is formatted this way, call:
python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/dataThis will combine each pair of images (A,B) into a single image file, ready for training.
- Our dataset is a part of The Kvasir Dataset.
- The data split we used in training can be downloaded here.
- After downloading the dataset, to create 5 folds:
cd EndoL2H/datasets
python 5_fold.pyData needs to be arranged in the following format:
EndoL2H # Path to main folder
└── datasets # Folder of all datasets
└── dataset_xxx # Name of a dataset
|
├── A # High resolution images
| ├── fold1
| | ├──train
| | | ├──1.jpg
| | | ├──2.jpg
| | | ├── ...
| | ├── test
| | └── val
| ├── fold2
| |
| ├── fold3
| |
| ├── fold4
| |
| └── fold5
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└── B # Low resolution images
├── fold1
| ├──train
| | ├──1.jpg
| | ├──2.jpg
| | ├── ...
| ├── test
| └── val
├── fold2
|
├── fold3
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├── fold4
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└── fold5
└── checkpoints
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└── generator_name #e.g. unet256, unet128, resnet_6blocks, resnet_9blocks
├── web
| ├── images
| | ├── epoch001_fake_B.png
| | ├── epoch001_real_A.png
| | ├── epoch001_real_B.png
| | ├── ...
| └── index.html
|
├── latest_net_D.pth
├── latest_net_G.pth
├── opt.txt
└── loss_log.txtTo train a model:
python train.py --dataroot ./datasets/${nameOfDataset} --name unet_256 --model pix2pix --netG unet_256 --dataset_mode aligned --direction BtoA --preprocess none- To see more intermediate results, check out
./checkpoints/unet_256/web/index.html. - To view training results and loss plots, run
python -m visdom.serverand click the URL http://localhost:8097.
To test the model:
python test.py --dataroot ./datasets/${nameOfDataset} --name unet_256 --model pix2pix --netG unet_256 --dataset_mode aligned --direction BtoA --preprocess none- The test results will be saved to a html file here:
./results/unet_256/test_latest/index.html.
Super-resolution results for EndoL2H, DBPN, RCAN and SRGAN with cropped and zoomed regions inside the yellow squares on 8×upscale factor and for the five classes of the dataset, abnormal classes: esophagitis (inflammatory disease of esophagus), polyps (abnormal growth of mucous membrane of small and large intestine), ulcerative colitis (inflammatory bowel disease), z-line (gastroesophageal junction images where the esophagus meetsthe stomach) and for pylorus images, respectively.
Each image pair consists of LR images and corresponding SAB output visualised using Grad-CAMmethodology [50]. The main aim of SAB is to selectively put emphasis on more distinctive regions with unusual gradientalterations that inherently deserves attention as suspicious areas, in this case for example, regions with polyps, inflammatorytissue or normal tissue regions containing more texture and sharp edges.
a) Resulting GMSD maps. Red color denotes higher GMSD values indicating low structural similarity with the original image and blue color represents low GMSD values indicating a high structural similarity with the original image.
b) Resulting SSIM heat maps. Red color denotes lower SSIM values representing a low structural similarity with the original image and blue color represent high SSIM values representing a high structural similarity with the original image.
PSNR and SSIM results of EndoL2H algorithm are provided in psnr_ssim_calculations.ipynb. After testing the algorithm, to see the results run it on Jupyter Notebook.
The GMSD, LPIPS, PSNR and SSIM z-scores of the algorithms EndoL2H, DBPN, RCAN and SRGAN are provided here. To see the statistical significance analysis results between EndoL2H and the other algorithms, you can run significance_analysis.ipynb on Jupyter Notebook.
You can download our pretrained model here.
- The pretrained model is saved at
./checkpoints/unet_256/latest_net_G.pth.
To compare the results of our model with DBPN and RCAN, you can download pretrained models of these two algorithms here.
This project is licensed under the MIT License - see the LICENSE file for details
This repository is based on pytorch-CycleGAN-and-pix2pix.
If you find our work useful in your research please consider citing our paper:
@article{almalioglu2020endol2h,
title={EndoL2H: Deep Super-Resolution for Capsule Endoscopy},
author={Yasin Almalioglu and Kutsev Bengisu Ozyoruk and Abdulkadir Gokce and Kagan Incetan and Guliz Irem Gokceler and Muhammed Ali Simsek and Kivanc Ararat and Richard J. Chen and Nichalos J. Durr and Faisal Mahmood and Mehmet Turan},
journal={arXiv preprint arXiv:2002.05459},
year={2020}
}
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