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

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.

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.

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

The datasets of the fundus images can be acquired from:

1. HRF
2. DRIVE
3. STARE

The trained models are present in Trained models folder.

[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.