Ultrasound nerve segmentation using Keras

Kaggle Ultrasound Nerve Segmentation competition [Keras]

#Install (Ubuntu {14,16}, GPU)

cuDNN required.

###Theano

http://deeplearning.net/software/theano/install_ubuntu.html#install-ubuntu
sudo pip install pydot-ng

In ~/.theanorc

[global]
device = gpu0
[dnn]
enabled = True

###Keras

sudo apt-get install libhdf5-dev
sudo pip install h5py
sudo pip install pydot
sudo pip install nose_parameterized
sudo pip install keras

In ~/.keras/keras.json

{
    "image_dim_ordering": "th",
    "epsilon": 1e-07,
    "floatx": "float32",
    "backend": "theano"
}

###Python deps

sudo apt-get install python-opencv
sudo apt-get install python-sklearn

#Prepare

Place train and test data into '../train' and '../test' folders accordingly.

mkdir np_data
python data.py

#Training

Single model training.

python train.py

Results will be generatated in "res/" folder. res/unet.hdf5 - best model

Generate submission:

python submission.py

Generate predection with a model in res/unet.hdf5

python current.py

#Model

I used U-net like architecture (http://arxiv.org/abs/1505.04597). Main differences:

inception blocks instead of VGG like
Conv with stride instead of MaxPooling
Dropout, p=0.5
skip connections from encoder to decoder layers with residual blocks
BatchNorm everywhere
2 heads training: auxiliary branch for scoring nerve presence (in the middle of the network), one branch for segmentation
ELU activation
sigmoid activation in output
Adam optimizer, without weight regularization in layers
Dice coeff loss, average per batch, without smoothing
output layers - sigmoid activation
batch_size=64,128 (for GeForce 1080 and Titan X respectively)

Augmentation:

flip x,y
random zoom
random channel shift
elastic transormation didn't help in this configuration

Augmentation generator (generate augmented data on the fly for each epoch) didn't improve the score. For prediction augmented images were used.

Validation:

For some reason validation split by patient (which is proper in this competition) didn't work for me, probably due to bug in the code. So I used random split.

Final prediction uses probability of a nerve presence: p_nerve = (p_score + p_segment)/2, where p_segment based on number of output pixels in the mask.

#Results and technical aspects

On GPU Titan X an epoch took about 6 minutes. Training early stops at 15-30 epochs.
For batch_size=64 6Gb GPU memory is required.
Best single model achieved 0.694 LB score.
An ensemble of 6 different k-fold ensembles (k=5,6,8) scored 0.70399

#Credits This code was originally based on https://github.com/jocicmarko/ultrasound-nerve-segmentation/

chu-NMSU/ultrasound-nerve-segmentation-1

Ultrasound nerve segmentation using Keras