Implementation of deep learning framework -- Unet, using Keras

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

Overview

Data

The original dataset is from isbi challenge, and I've downloaded it and done the pre-processing.

Data augmentation

The data for training contains 30 512*512 images, which are far not enough to feed a deep learning neural network. I use a module called ImageDataGenerator in keras.preprocessing.image to do data augmentation.

See dataPrepare.ipynb and data.py for detail.

Model

This deep neural network is implemented with Keras functional API, which makes it extremely easy to experiment with different interesting architectures.

Output from the network is a 512*512 which represents mask that should be learned. Sigmoid activation function makes sure that mask pixels are in [0, 1] range.

Training

The model is trained for 5 epochs.

After 5 epochs, calculated accuracy is about 0.97.

Loss function for the training is basically just a binary crossentropy.

How to use

Dependencies

Tensorflow
Keras >= 1.0

Run main.py

You will see the predicted results of test image in data/membrane/test

benprano / U-Net-Convolutional-Networks-for-Biomedical-Image-Segmentation.