Ultrasound nerve segmentation using Keras (1.0.7)

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 (it's very important, the project was running on theano backend, and some issues are possible in case of TensorFlow)

{
    "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

Motivation's explained in my internal pres (slides: http://www.slideshare.net/Eduardyantov/ultrasound-segmentation-kaggle-review)

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/