Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images.
See the application of the model in Demo folder.
Implemented in Keras(2.0.4) with TensorFlow(1.1.0) as backend.
Use of data augmentation for training required slight changes to keras ImageDataGenerator. Generator in image_gen.py applies same transformation to both the image and the label mask.
To use this implementation one needs to load and preprocess data (see load_data.py), train new model if needed (train_model.py) and use the model for generating lung masks (inference.py).
trained_model.hdf5 contains model trained on both data sets mentioned below.
Scores achieved on Montgomery and JSRT(With these masks. See preprocess_JSRT.py.) (Measured using 5-fold cross-validation):
| JSRT | Montgomery | |
|---|---|---|
| IoU | 0.971 | 0.956 |
| Dice | 0.985 | 0.972 |
