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https://github.com/arkanivasarkar/retinal-vessel-segmentation-using-variants-of-unet

Retinal vessel segmentation using U-NET, Res-UNET, Attention U-NET, and Residual Attention U-NET (RA-UNET)
https://github.com/arkanivasarkar/retinal-vessel-segmentation-using-variants-of-unet

attention-unet computer-vision image-processing intersection-over-union jaccard-coefficient jaccard-loss keras keras-tensorflow patchwise-training residual-attention-unet residual-unet retinal-fundus-images retinal-vessel-segmentation unet

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Retinal vessel segmentation using U-NET, Res-UNET, Attention U-NET, and Residual Attention U-NET (RA-UNET)

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README 

        
# Retinal-Vessel-Segmentation-using-Variants-of-UNET



This repository contains the implementation of fully convolutional neural networks for segmenting retinal vasculature from fundus images.



![alt text](https://i.ibb.co/BKr7cVF/Picture1.png) 



Four architecures/models were made keeping U-NET architecture as the base.

The models used are:

- Simple U-NET

- Residual U-NET (Res-UNET)

- Attention U-NET

- Residual Attention U-NET (RA-UNET)



The performance metrics used for evaluation are accuracy and mean IoU.



## Methods

Images from HRF, DRIVE and STARE datasets are used for training and testing. The following pre-processing steps are applied before training the models:

- Green channel selection

- Contrast-limited adaptive histogram equalization (CLAHE)

- Cropping into non-overlapping patches of size 512 x 512



10 images from DRIVE and STARE and 12 images from HRF was kept for testing the models. The training dataset was then split into 70:30 ratio for training and validation.



Adam optimizer with a learning rate of 0.001 was used as optimizer and IoU loss was used as the loss function. The models were trained for 150 epochs with a batch size of 16, using NVIDIA Tesla P100-PCIE GPU. 



## Results

The performance of the models were evaluated using the test dataset.

Out of all the models, Attention U-NET achieved a greater segmentation performance. 



The following table compares the performance of various models



| **Datasets** |    **Models**    | **Average Accuracy**| **Mean IoU**|

|:------------:|:----------------:|:-------------------:|:-----------:|

| HRF          | Simple U-NET     | 0.965               |0.854        |

| HRF          | Res-UNET         | 0.964               |0.854        |

| HRF          | Attention U-NET  | 0.966               |0.857        |

| HRF          | RA-UNET          | 0.963               |0.85         |

| DRIVE        | Simple U-NET     | 0.9                 |0.736        |

| DRIVE        | Res-UNET         | 0.903               |0.741        |

| DRIVE        | Attention U-NET  | 0.905               |0.745        |

| DRIVE        | RA-UNET          | 0.9                 |0.735        |

| STARE        | Simple U-NET     | 0.882               |0.719        |

| STARE        | Res-UNET         | 0.893               |0.737        |

| STARE        | Attention U-NET  | 0.893               |0.738        |

| STARE        | RA-UNET          | 0.891               |0.733        |



![alt text](https://i.ibb.co/W07sGYv/Picture3.png)



### Datasets

The datasets of the fundus images can be acquired from:

1. [HRF](https://www5.cs.fau.de/research/data/fundus-images/)

2. [DRIVE](http://www.isi.uu.nl/Research/Databases/DRIVE/)

3. [STARE](https://cecas.clemson.edu/~ahoover/stare/)



The trained models are present in `Trained models` folder.



## References



[1] Vengalil, Sunil Kumar & Sinha, Neelam & Kruthiventi, Srinivas & Babu, R. (2016). Customizing CNNs for blood vessel segmentation from fundus images. 1-4. 10.1109/SPCOM.2016.7746702..



[2] Ronneberger O., Fischer P., Brox T. U-Net: Convolutional networks for biomedical image segmentation International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2015), pp. 234-241



[3] Zhang, Zhengxin & Liu, Qingjie. (2017). Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters. PP. 10.1109/LGRS.2018.2802944.



[4] Oktay, Ozan & Schlemper, Jo & Folgoc, Loic & Lee, Matthew & Heinrich, Mattias & Misawa, Kazunari & Mori, Kensaku & McDonagh, Steven & Hammerla, Nils & Kainz, Bernhard & Glocker, Ben & Rueckert, Daniel. (2018). Attention U-Net: Learning Where to Look for the Pancreas.



[5] Ni, Zhen-Liang & Bian, Gui-Bin & Zhou, Xiao-Hu & Hou, Zeng-Guang & Xie, Xiao-Liang & Wang, Chen & Zhou, Yan-Jie & Li, Rui-Qi & Li, Zhen. (2019). RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments.



[6] Jin, Qiangguo & Meng, Zhaopeng & Pham, Tuan & Chen, Qi & Wei, Leyi & Su, Ran. (2018). DUNet: A deformable network for retinal vessel segmentation.



 

 

 

 



 This project is done during Indian Academy of Sciences Summer Reasearch Fellowship '21