Ultrasound Nerve Segmentation using U-Net

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation.

This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good starting point for further, more serious approaches.

The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Segmentation.

How to use

Dependencies

This tutorial depends on the following libraries:

scikit-image
Tensorflow
Keras >= 2.0

Also, this code should be compatible with Python versions 2.7-3.5.

Prepare the data

In order to extract raw images and save them to .npy files, you should first prepare its structure. Make sure that raw dir is located in the root of this project. Also, the tree of raw dir must be like:

-raw
 |
 ---- train
 |    |
 |    ---- 1_1.tif
 |    |
 |    ---- …
 |
 ---- test
      |
      ---- 1.tif
      |
      ---- …

Now run python data.py.

Running this script will create train and test images and save them to .npy files.

Define the model

Check out get_unet() in train.py to modify the model, optimizer and loss function.

Train the model and generate masks for test images

Run python train.py to train the model.

Check out train_predict() to modify the number of iterations (epochs), batch size, etc.

After this script finishes, in imgs_mask_test.npy masks for corresponding images in imgs_test.npy should be generated. I suggest you examine these masks for getting further insight of your model's performance.

nikheelpandey / UNet-Keras