Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images

TL;DR

This paper introduces a hierarchical SegNet model enhanced with channel and context attention mechanisms, significantly improving lung segmentation accuracy in chest X-ray images for better diagnosis.

Contribution

It presents a novel multi-modal attention mechanism integrated into Hierarchical SegNet, which improves feature representation and segmentation performance in medical imaging.

Findings

Achieved state-of-the-art lung segmentation accuracy

Outperformed existing methods in experimental tests

Enhanced feature capture through combined attention mechanisms

Abstract

Lung segmentation in chest X-ray images is a critical task in medical image analysis, enabling accurate diagnosis and treatment of various lung diseases. In this paper, we propose a novel approach for lung segmentation by integrating Hierarchical SegNet with a proposed multi-modal attention mechanism. The channel attention mechanism highlights specific feature maps or channels crucial for lung region segmentation, while the context attention mechanism adaptively weighs the importance of different spatial regions. By combining both mechanisms, the proposed mechanism enables the model to better capture complex patterns and relationships between various features, leading to improved segmentation accuracy and better feature representation. Furthermore, an attention gating mechanism is employed to integrate attention information with encoder features, allowing the model to adaptively weigh the importance of different attention features and ignore irrelevant ones. Experimental results demonstrate that our proposed approach achieves state-of-the-art performance in lung segmentation tasks, outperforming existing methods. The proposed approach has the potential to improve the accuracy and efficiency of lung disease diagnosis and treatment, and can be extended to other medical image analysis tasks.