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

arXiv: https://arxiv.org/abs/2009.05752 · HTML: https://mlsanity.com/paper/2009.05752/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models](https://mlsanity.com/paper/2211.00902/) (2022)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays](https://mlsanity.com/paper/2106.05915/) (2021)
- [Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices](https://mlsanity.com/paper/2206.02358/) (2022)
- [Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review](https://mlsanity.com/paper/2205.07236/) (2022)
- [Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection](https://mlsanity.com/paper/2201.09563/) (2022)
- [Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography](https://mlsanity.com/paper/2402.11510/) (2024)

## Abstract (arXiv)

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.