Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.

Introduction: Lung field segmentation (LFS) in chest x-rays (CXR) is a key step for the computer-assisted diagnosis of respiratory diseases. However, achieving precise delineation remains challenging in images with poor contrast and superimposed anatomical structures, often resulting in imprecise lung outlines in deep learning (DL) models. Furthermore, existing models often lack generalizability, performing well on specific datasets but failing on unseen domains due to variations in imaging equipment, acquisition protocols, and patient demographics. Methods: To overcome these limitations, we propose a two-phase hybrid approach for robust and generalizable LFS in CXR. The first phase employs a heuristic approach to extract and refine multiscale edge features: The Laplacian of Gaussian (LoG) filter detects closed contours that effectively highlight the overall lung shape, whereas the Canny detector captures the finer, high-frequency edge details that preserve the lung outlines often missed by DL models due to overlapping structures. In the second phase, the enriched multiscale feature maps are fused along with the original contrast inputs and then used to train and evaluate various U-Net architectures. Results: The proposed approach is evaluated on three benchmark datasets: Montgomery County (MC), Shenzhen Hospital (SH), and Japanese Society of Radiological Technology (JSRT). Experiments conducted on individual datasets using five-fold cross-validation and testing on a completely unseen separate test set to simulate real-world settings revealed that the proposed edge-assisted filters, especially the Canny filter, significantly improve segmentation performance metrics. Among all U-Net variants, deep attention U-Net achieved the highest performance using a consistent channel combination, attaining a dice coefficient of 0.9815, a Jaccard score of 0.9624 on the JSRT dataset. The proposed approach achieved a dice gain of +0.0011 to +0.0034 and an IoU gain of +0.009 to +0.0034 across the three datasets compared with the baseline configuration using original images. Furthermore, cross-dataset validation also depicted improvement gains in dice, IoU, and other metrics, demonstrating the generalizability of the proposed approach. Conclusion: Further cross-dataset validation confirmed the framework's strong generalization capabilities, demonstrating stable performance even on unseen domains. These findings validate the proposed method as a reliable and effective solution for LFS in diverse and clinically challenging environments, offering promising benefits for respiratory diagnosis and patient care.