https://github.com/orobix/retina-unet
20幅训练，20幅测试，图像大小768*584.
https://www.isi.uu.nl/Research/Databases/DRIVE/
Methods
Before training, the 20 images of the DRIVE training datasets are pre-processed with the following transformations:
- Gray-scale conversion
- Standardization
- Contrast-limited adaptive histogram equalization (CLAHE)
- Gamma adjustment
The training of the neural network is performed on sub-images (patches) of the pre-processed full images. Each patch, of dimension 48x48, is obtained by randomly selecting its center inside the full image. Also the patches partially or completely outside the Field Of View (FOV) are selected, in this way the neural network learns how to discriminate the FOV border from blood vessels.
A set of 190000 patches is obtained by randomly extracting 9500 patches in each of the 20 DRIVE training images. Although the patches overlap, i.e. different patches may contain same part of the original images, no further data augmentation is performed. The first 90% of the dataset is used for training (171000 patches), while the last 10% is used for validation (19000 patches).
The neural network architecture is derived from the U-net architecture (see the paper). The loss function is the cross-entropy and the stochastic gradient descent is employed for optimization. The activation function after each convolutional layer is the Rectifier Linear Unit (ReLU), and a dropout of 0.2 is used between two consecutive convolutional layers.
Training is performed for 150 epochs, with a mini-batch size of 32 patches. Using a GeForce GTX TITAN GPU the training lasts for about 20 hours.
Results on DRIVE database
Testing is performed with the 20 images of the DRIVE testing dataset, using the gold standard as ground truth. Only the pixels belonging to the FOV are considered. The FOV is identified with the masks included in the DRIVE database.
In order to improve the performance, the vessel probability of each pixel is obtained by averaging multiple predictions. With a stride of 5 pixels in both height and width, multiple consecutive overlapping patches are extracted in each testing image. Then, for each pixel, the vessel probability is obtained by averaging probabilities over all the predicted patches covering the pixel.