Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
arXiv:2009.05752 · doi:10.1109/ACCESS.2020.3017915
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.
Volume 8, August 2020, Pages 153535 - 153545
References in corpus (5)
- Fully Convolutional Networks for Semantic Segmentation
- Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
- Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
- Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images
- Automatic Liver Segmentation Using an Adversarial Image-to-Image Network