Please put the images in the corresponding folders.

• Testing: Mask images for testing. The name format should look like "image-0-srf-grader1-1.png"
• Valid_Raw: Raw images for testing. The name format should look like "image-0.png"
• Training: Mask images for training. The name format should look like "image-0-srf-grader1-1.png"
• Train_Raw: Raw images for training. The name format should look like "image-0.png"

• Use constants.py to change different setting such as enabling/disabling augmentation. The augmentation is turned off in default. Please turn it on to obtain the best result (but this would increase the training time significantly).
• Use unet.py to test different models. The basic model corresponds to get_unet_shallow() in our study. The nested model corresponds to get_unet_inner(). The original U-net model corresponds to get_unet().
• Use loss.py to test different loss function. The best loss function is weighted_bce_dice_loss().
  To run the model, use python3 main.py.

• We have tested our model configuration using public available dataset.
• Please download it following this and put the data into ultrasound-nerve-segmentation folder. Note that since the masks for testing images are not provided by the data provider. Train/valid/test split is done using training images only.
• Modify constants.py file to change the image_rows and image_cols to 420, 580 respectively. And change img_row, img_cols to 96, 128 respectively.
• Run data_new.py to read the image data into npy file.
• Then run train_new.py to train using ultrasound nerve segmentation data.