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Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)
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# ResUNet++: An advanced architecture for medical image segmentation
Tensoflow implementation of ResUNet++
# ResUNet++
The ResUNet++ architecture is based on the Deep Residual U-Net (ResUNet), which is an architecture that uses the strength of deep residual learning and U-Net. The proposed ResUNet++ architecture takes advantage of the residual blocks, the squeeze and excitation block, ASPP, and the attention block.
More description about the archicture can be in the paper [ResUNet++: An Advanced Architecture for Medical Image Segmentation] (https://arxiv.org/pdf/1911.07067.pdf).
## Architecture
<p align="center">
<img src="Resunet++.png">
</p>
## Requirements:
os
numpy
cv2
tensorflow
glob
tqdm
## Folders:
data: Contains the set of three dataset as mentioned.
files: Contains the csv file and weight file generated during training.
new_data: Contains two subfolder `images` and `masks`, they contains the augmented images and masks.
## Files:
1. process_image.py: Augment the images and mask for the training dataset.
2. data_generator.py: Dataset generator for the keras.
3. infer.py: Run your model on test dataset and all the result are saved in the result` folder. The images are in the sequence: Image,Ground Truth Mask, Predicted Mask.
4. run.py: Train the unet.
5. unet.py: Contains the code for building the UNet architecture.
6. resunet.py: Contains the code for building the ResUNet architecture.
7. m_resunet.py: Contains the code for building the ResUNet++ architecture.
8. mertrics.py: Contains the code for dice coefficient metric and dice coefficient loss.
## First check for the correct path and the patameters.
1. python3 process_image.py - to augment training dataset.
2. python3 run.py - to train the model.
3. python3 infer.py - to test and generate the mask.
## Pytorch implementation of ResUNet++ can be found here :
https://github.com/rishikksh20/ResUnet/blob/master/core/res_unet_plus.py
## Dataset Link
[Kvasir-SEG](https://datasets.simula.no/kvasir-seg/)
## Results
Qualitative results comparison on the Kvasir-SEG dataset.From the left: image (1), (2) Ground truth, (3) U-Net, (4)
ResUNet, (5) ResUNet-mod, and (6) ResUNet++. <br/>
<p align="center">
<img src="results.png">
</p>
## Citation
Please cite our paper if you find the work useful:
<pre>
@INPROCEEDINGS{8959021,
author={D. {Jha} and P. H. {Smedsrud} and M. A. {Riegler} and D. {Johansen} and T. D. {Lange} and P. {Halvorsen} and H. {D. Johansen}},
booktitle={Proceedings of the IEEE International Symposium on Multimedia (ISM)},
title={ResUNet++: An Advanced Architecture for Medical Image Segmentation},
year={2019},
pages={225-230}}
</pre>
## Contact
Please contact [email protected] for any further questions.
Excerpt of 2,768 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3e31ee896aff7ad2, topic:pytorch, topic:tensorflow
matched fp:3e31ee896aff7ad2, topic:image-segmentation, desc:image segmentation, readme:image segmentation