Lung fields segmentation on CXR images using convolutional neural networks.
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README.md

Lung Segmentation (2D)

Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images.

Demo

See the application of the model in Demo folder.

Implementation

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.

Segmentation

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