Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition conditions. We propose AnyCXR, a unified framework that enables generalizable multi-organ segmentation across arbitrary CXR projection angles using only synthetic supervision. The method combines a Multi-stage Domain Randomization (MSDR) engine, which generates over 100,000 anatomically faithful and highly diverse synthetic radiographs from 3D CT volumes, with a Conditional Joint Annotation Regularization (CAR) learning strategy that leverages partial and imperfect labels by enforcing anatomical consistency in a latent space. Trained entirely on synthetic data, AnyCXR achieves strong zero-shot generalization on multiple real-world datasets, providing accurate delineation of 54 anatomical structures in PA, lateral, and oblique views. The resulting segmentation maps support downstream clinical tasks, including automated cardiothoracic ratio estimation, spine curvature assessment, and disease classification, where the incorporation of anatomical priors improves diagnostic performance. These results demonstrate that AnyCXR establishes a scalable and reliable foundation for anatomy-aware CXR analysis and offers a practical pathway toward reducing annotation burdens while improving robustness across diverse imaging conditions.

翻译：由于全面标注数据的稀缺性以及真实世界采集条件的显著差异性，胸部X射线（CXR）的鲁棒解剖分割仍具挑战性。我们提出AnyCXR，一个仅使用合成监督即可实现跨任意CXR投影角度通用多器官分割的统一框架。该方法结合了多阶段域随机化（MSDR）引擎与条件联合标注正则化（CAR）学习策略。MSDR引擎从3D CT体数据生成超过10万张解剖结构真实且高度多样化的合成放射影像；CAR学习策略则通过在潜在空间中强制解剖一致性，有效利用部分及不完美的标注数据。AnyCXR完全在合成数据上训练，在多个真实世界数据集上实现了强大的零样本泛化能力，能够准确描绘PA位、侧位及斜位视图中的54个解剖结构。所得分割图支持下游临床任务，包括自动心胸比估计、脊柱曲度评估及疾病分类，其中解剖先验的融入提升了诊断性能。这些结果表明，AnyCXR为解剖感知的CXR分析建立了可扩展且可靠的基础，并为减轻标注负担、同时提升跨多样化成像条件的鲁棒性提供了一条实用路径。