Generative AI: A Pix2pix-GAN-Based Machine Learning Approach for Robust and Efficient Lung Segmentation

TL;DR

This paper introduces a novel deep learning framework based on Pix2pix GAN for accurate and robust lung segmentation in chest X-ray images, aiming to improve early disease detection and reduce radiologist workload.

Contribution

It develops a new GAN-based segmentation model combining preprocessing, augmentation, and a U-Net-inspired architecture, tested on Montgomery and Shenzhen datasets for robustness.

Findings

High accuracy in lung segmentation metrics

Effective generalization to unseen datasets

Potential for clinical application in early disease detection

Abstract

Chest radiography is climacteric in identifying different pulmonary diseases, yet radiologist workload and inefficiency can lead to misdiagnoses. Automatic, accurate, and efficient segmentation of lung from X-ray images of chest is paramount for early disease detection. This study develops a deep learning framework using a Pix2pix Generative Adversarial Network (GAN) to segment pulmonary abnormalities from CXR images. This framework's image preprocessing and augmentation techniques were properly incorporated with a U-Net-inspired generator-discriminator architecture. Initially, it loaded the CXR images and manual masks from the Montgomery and Shenzhen datasets, after which preprocessing and resizing were performed. A U-Net generator is applied to the processed CXR images that yield segmented masks; then, a Discriminator Network differentiates between the generated and real masks. Montgomery dataset served as the model's training set in the study, and the Shenzhen dataset was used to test its robustness, which was used here for the first time. An adversarial loss and an L1 distance were used to optimize the model in training. All metrics, which assess precision, recall, F1 score, and Dice coefficient, prove the effectiveness of this framework in pulmonary abnormality segmentation. It, therefore, sets the basis for future studies to be performed shortly using diverse datasets that could further confirm its clinical applicability in medical imaging.