RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection

Anatomical structure labels for radiographs are essential for medical image segmentation and a broad range of downstream diagnostic tasks. However, annotating anatomy directly on 2D radiographs is intrinsically ambiguous, as 3D anatomical structures are projected onto a single 2D plane where boundaries may overlap, be occluded, or appear only partially visible. These ambiguities complicate the construction of datasets with broad anatomical coverage and consistent labels. To address these limitations, we introduce ***RadGenome-Anatomy***, the largest anatomy-labeled chest radiograph dataset, containing over **10 million** segmentation masks across **210** anatomical structures in $25,692$ studies. It is constructed by projecting large-scale 3D anatomical masks from CT volumes into 2D radiographic space through canonical radiographic geometry. This shifts annotation from directly tracing uncertain 2D boundaries to defining anatomy in volumetric space, where structures that overlap or become partially invisible in radiographs remain spatially separable. As a result, each 2D mask represents the physically grounded projected footprint of a volumetrically defined structure. The scale and broad anatomical coverage of *RadGenome-Anatomy*, including structures that are overlapping, partially visible, or difficult to delineate directly, enable research on geometric measurements as explicit evidence for radiographic interpretation. We demonstrate this by training *XAnatomy* to predict structure-specific masks and derive clinically relevant measurements, achieving diagnostic accuracies of $96.4\%$, $95.6\%$, and $89.2\%$ for cardiomegaly, kyphosis, and scoliosis, respectively.