Automatic lung segmentation in the chest x-ray is important for computer aided diagnosis. It helps in the surgical planning and diagnosis of pulmonary diseases. Lung shape, size, overlapped area, and opacities make lung segmentation arduous. In this article, we have proposed a UNet-based model for lung segmentation. We have evaluated the model on difficult datasets that have chest radiographs of patients affected by tuberculosis and other severe abnormalities. Three chest radiography datasets and a CT-scan dataset are used to prove the model generalization. The proposed model efficiently uses the residual learning and attention mechanisms to improve the segmentation results against the original UNet for the dice coefficient index (DCI) and Jaccard index. We have also performed an ablation study to highlight the impact of the attention mechanism in the proposed model. The model obtained a 97.62% DCI, 95.43% Jaccard index, and a 4.00 Hausdorff distance on the Montgomery County dataset. While on the Shenzhen and NIH datasets, it achieved a 95.71% and 95.75% DCI, 91.90% and 91.95% Jaccard index, and a 5.23 and 5.20 Hausdorff distance, respectively. The proposed model has achieved better or comparable performance against other state-of-the-art models.

中文翻译：

---

ReSE-Net：增强型 UNet 架构，用于胸部放射成像图像中的肺部分割

胸部 X 光片中的自动肺部分割对于计算机辅助诊断非常重要。它有助于肺部疾病的手术计划和诊断。肺的形状、大小、重叠区域和不透明度使肺分割变得困难。在本文中，我们提出了一种基于 UNet 的肺部分割模型。我们在困难的数据集上评估了该模型，这些数据集包含受结核病和其他严重异常影响的患者的胸片。三个胸部放射线摄影数据集和一个 CT 扫描数据集用于证明模型的泛化性。所提出的模型有效地使用残差学习和注意力机制来改进原始 UNet 的骰子系数索引 (DCI) 和 Jaccard 索引的分割结果。我们还进行了一项消融研究，以强调所提出模型中注意力机制的影响。该模型在蒙哥马利县数据集上获得了 97.62% DCI、95.43% Jaccard 指数和 4.00 Hausdorff 距离。在深圳和 NIH 数据集上，其 DCI 分别为 95.71% 和 95.75%，Jaccard 指数分别为 91.90% 和 91.95%，Hausdorff 距离分别为 5.23 和 5.20。与其他最先进的模型相比，所提出的模型取得了更好或相当的性能。