PAX-Ray++ dataset

Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we propose a novel method for extracting fine-grained anatomical structures in CXR using pseudo-labeling of three-dimensional computed tomography (CT) scans. Methods: We created a large-scale dataset of 10,021 thoracic CTs with 157 labels and applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. These labels were projected onto a two-dimensional plane, similar to the CXR, allowing the training of detailed semantic segmentation models for CXR without any manual annotation effort. Results: Our resulting segmentation models demonstrated remarkable performance on CXR, with a high average model-annotator agreement between two radiologists with mIoU scores of 0.93 and 0.85 for frontal and lateral anatomy, while inter-annotator agreement remained at 0.95

目的：由于肺部、心脏和骨骼等重叠结构的模糊性，解读胸部X光片（CXR）一直是一项具有挑战性的任务。为解决这一问题，我们提出了一种新颖的方法，通过三维计算机断层扫描（CT）的伪标签提取CXR中的细粒度解剖结构。方法：我们创建了一个包含10,021例胸腔CT扫描的大规模数据集，并应用了一组三维解剖分割模型以提取解剖伪标签。这些标签被投射到类似于CXR的二维平面上，从而允许在不进行任何手动标注的情况下训练详细的语义分割模型。结果：我们的分割模型在CXR上表现出卓越的性能，两名放射科医生之间的高平均模型标注者一致性达到0.93和0.85（分别对应于正面和侧面解剖结构），而标注者之间的内部一致性保持在0.95。