Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation

TL;DR

This paper introduces a novel deep learning method combining Attention U-Net and adversarial training for lung segmentation in chest X-rays, achieving high accuracy and generalization across datasets.

Contribution

The paper proposes a new adversarial architecture integrated with Attention U-Net for improved lung segmentation in chest X-ray images.

Findings

Achieved a DSC of 97.5% on the JSRT dataset.

Generalized well to unseen datasets with different patient profiles.

Enhanced segmentation accuracy using adversarial training.

Abstract

Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.