Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opac- ities in CXRs frequently obscure anatomical structures, impeding clear identification of lung borders and complicating the localization of pathology. This challenge significantly hampers segmentation accuracy and precise lesion identification, which are crucial for diagnosis. To tackle these issues, our study proposes an unpaired CXR translation framework that converts CXRs with lung opacities into counterparts without lung opacities while preserving semantic features. Central to our approach is the use of adaptive activation masks to selectively modify opacity regions in lung CXRs. Cross-domain alignment ensures translated CXRs without opacity issues align with feature maps and prediction labels from a pre-trained CXR lesion classifier, facilitating the interpretability of the translation process. We validate our method using RSNA, MIMIC-CXR-JPG and JSRT datasets, demonstrating superior translation quality through lower Frechet Inception Distance (FID) and Kernel Inception Distance (KID) scores compared to existing meth- ods (FID: 67.18 vs. 210.4, KID: 0.01604 vs. 0.225). Evaluation on RSNA opacity, MIMIC acute respiratory distress syndrome (ARDS) patient CXRs and JSRT CXRs show our method enhances segmentation accuracy of lung borders and improves lesion classification, further underscoring its potential in clinical settings (RSNA: mIoU: 76.58% vs. 62.58%, Sensitivity: 85.58% vs. 77.03%; MIMIC ARDS: mIoU: 86.20% vs. 72.07%, Sensitivity: 92.68% vs. 86.85%; JSRT: mIoU: 91.08% vs. 85.6%, Sensitivity: 97.62% vs. 95.04%). Our approach advances CXR imaging analysis, especially in investigating segmentation impacts through image translation techniques.

翻译：胸部X光片（CXRs）在诊断和监测心肺疾病中发挥着关键作用。然而，CXRs中的肺不透明区域常常掩盖解剖结构，阻碍了肺边界的清晰识别，并使病理定位复杂化。这一挑战严重影响了分割精度和精确的病灶识别，而这两者对诊断至关重要。为解决这些问题，本研究提出了一种非配对CXR转换框架，该框架能够将带有肺不透明区域的CXRs转换为不带有肺不透明区域的对应图像，同时保留语义特征。我们方法的核心是使用自适应激活掩码来选择性修改肺部CXRs中的不透明区域。跨域对齐确保转换后的无异常CXRs与预训练的CXR病灶分类器的特征图和预测标签保持一致，从而提高了转换过程的可解释性。我们使用RSNA、MIMIC-CXR-JPG和JSRT数据集验证了我们的方法，通过较低的弗雷歇起始距离（FID）和核起始距离（KID）分数证明了其优于现有方法的转换质量（FID：67.18 对比 210.4，KID：0.01604 对比 0.225）。在RSNA不透明数据集、MIMIC急性呼吸窘迫综合征（ARDS）患者CXRs和JSRT CXRs上的评估表明，我们的方法提高了肺边界的分割精度并改善了病灶分类，进一步凸显了其在临床环境中的潜力（RSNA：mIoU：76.58% 对比 62.58%，敏感性：85.58% 对比 77.03%；MIMIC ARDS：mIoU：86.20% 对比 72.07%，敏感性：92.68% 对比 86.85%；JSRT：mIoU：91.08% 对比 85.6%，敏感性：97.62% 对比 95.04%）。我们的方法推进了CXR成像分析，特别是在通过图像转换技术研究分割影响方面。