Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
This work addresses a domain-specific problem for medical imaging by improving segmentation accuracy for respiratory disease diagnosis, but it is incremental as it applies an existing GAN method to a known task.
The paper tackled lung segmentation in chest X-ray images using generative adversarial networks (GANs), achieving a dice-score of 0.9740 and IOU score of 0.943, which outperformed other state-of-the-art results.
Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results.