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Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants fall short in addressing these challenges, primarily due to the limited ability to model long-range dependencies between image features. While vision transformers equipped with self-attention mechanisms excel at capturing long-range relationships, either a coarse-grained global self-attention or a fine-grained local self-attention is typically adopted for segmentation tasks on high-resolution images to alleviate quadratic computational cost at the expense of performance loss. This paper introduces a focal modulation UNet model (FMN-UNet) to enhance segmentation performance by effectively aggregating fine-grained local and coarse-grained global relations at a reasonable computational cost. FMN-UNet first encodes CXR images via a convolutional encoder to suppress background regions and extract latent feature maps at a relatively modest resolution. FMN-UNet then leverages global and local attention mechanisms to model contextual relationships across the images. These contextual feature maps are convolutionally decoded to produce segmentation masks. The segmentation performance of FMN-UNet is compared against state-of-the-art methods on three public CXR datasets (JSRT, Montgomery, and Shenzhen). Experiments in each dataset demonstrate the superior performance of FMN-UNet against baselines. [ABSTRACT FROM AUTHOR]

• A cascade framework is proposed for automatic lung segmentation in CXRs. • An adaptive closed polyline searching method is used to obtain data sequence. • An improved machine learning model is proposed to express a mathematical model. • The explainability-guided mathematical model is used to denote lung contour. • Performance of the proposed method is evaluated on public multi-site datasets. Large variations in anatomical shape and size, too much overlap between anatomical structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x-rays (CXR) a challenging problem. In this paper, we propose an automatic method called A-LugSeg that consists of two subnetworks for lung segmentation in CXRs. The first is a segmentation subnetwork based on a deep learning model (i.e., Mask-RCNN), which completes a coarse segmentation for each input CXR image. The second is a refinement subnetwork designed to optimize the coarse segmentation result by combining an improved closed principal curve method and an enhanced machine learning, where the lung contour's explainability-guided mathematical model is expressed by the machine learning's parameters. The performance of the proposed method is evaluated on three public datasets, namely the ShenZhen hospital Chest X-ray dataset (SZCX), Japanese Society of Radiological Technology dataset (JSRT), and Montgomery County chest x-ray dataset (MC), which contain the 662 CXRs, 247 CXRs, and 138 CXRs, respectively. We used different datasets for training/validation (SZCX) and testing (SZCX/JSRT/MC). Furthermore, we used six evaluation metrics to evaluate the performance of our proposed method, including Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), Accuracy (ACC), Precision, Sensitivity, and Specificity. The obtained results (DSC = 0.973, Ω = 0.958, ACC = 0.972, and p -value for DSC < 0.001) for JSRT, (DSC = 0.971, Ω = 0.955, ACC = 0.97, and p -value for DSC < 0.001) for MC, (DSC = 0.972, Ω = 0.956, and ACC = 0.97) for hybrid datasets (JSRT + MC), and (Precision, Sensitivity, and Specificity are higher than 0.98) show the superior performance of the proposed dual subnetwork segmentation algorithm compared to the existing state of the art approaches. [ABSTRACT FROM AUTHOR]