Chest X-ray procedures are considered to be the most popular for diagnosis of chest related diseases. We as a machine learning and medical imaging community, have seen extraordinary interest in the chest x-rays anlaysis and segmentation tasks. For any diagnosis on chest x-rays, accuracte segmentation of the biological object is fundamental.
Here, we show how we can use
Generative Adversarial Networks (GANs)to perform segmentation of lungs within chest x-rays.
The generator of the GAN generates a segmented mask of a given chest x-ray. Once the generator is trained to generate realistic looking masks, the GAN can now be used to perform segmentation of the lungs on new chest x-ray images.
Read the full paper at IEEEXplore.
The model may still perform over-segmentation or can miss some of the pixels. For example, see in Figure below.
