Ultrasound Nerve Segmentation

Semantic image segmentation of a collection of nerves called Brachial Plexus in ultrasound images

Environment Requirements

The following needs to be installed:

• Python 2.7, or >= 3.4
• numpy >= 1.11
• Tensorflow >= 0.9
• OpenCV >= 2.4
• tqdm >= 4.8

NOTE: tqdm can be installed using pip install tqdm

Downloading the Dataset

The dataset can be downloaded from Kaggle competetion "Ultrasound Nerve Segmation":

https://www.kaggle.com/c/ultrasound-nerve-segmentation/data

Download the train set and unzip the contents. Put the images (.tif files) in <root_project_dir>/data/train/.

Preprocessing

After downloading the dataset, run the preprocess routine by issuing the command:

python preprocess.py

After running the script, the following files will be generated:

data/train_xs96/        - Directory containing the filtered and resized images
data/train_set.npz      - Train set in numpy readable format
data/validation_set.npz - Validation set in numpy readable format
data/train_stats.pkl    - Pickle file that contains basic statistics about the train set

Training the Model

To train the model, execute the command:

python train.py

Training takes approximately 17 minutes per epoch on Amazon AWS g2.2xlarge instance with GPU support. A pre-trained model, that was ran for 10 hours with 35 epochs, can be downloaded at:

https://www.dropbox.com/s/xoib4r58hnbwkan/model-35.zip?dl=0

Extract the files and place the 'model.ckpt-35' and 'model.ckpt-35.meta' files in output/checkpoints/ directory.

Running Inference

To run inference, place the target images (.tif files) in a directory. If the images have accompanying ground truth labels, the labels must have the same name as the target and end with <name>_mask.tif.

Execute the command:

python inference.py -s <image_dir>

The generated output will be saved in <image_dir>_output

For more details on accepted arguments, see inference.py file.

A sample set of images can be found at sample_data/ directory. To see a smaple output, execute the command:

python inference.py -s sample_data/

The generated output can be found at sample_data_output/.