Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based method for lung segmentation in chest X-ray images, achieving high accuracy and outperforming existing methods.

Contribution

The work presents a novel application of GANs for lung segmentation in CXRs, with multiple discriminator models evaluated for improved performance.

Findings

Achieved a dice-score of 0.9740

Attained an IOU score of 0.943

Outperformed state-of-the-art methods

Abstract

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.