FleXray: Universal Clinical X-ray Segmentation
MIT CSAIL releases FleXray, a model that segments 60 anatomical structures in clinical X-rays.
FleXray is a generalist model for anatomical segmentation across the body in clinical X-rays, trained without large manually labeled X-ray sets. A physics-based engine uses existing 3D whole-body CT segmentations and generative image editing to simulate fully annotated 2D X-rays with varied appearance, physiology, and geometry. Trained on those simulations, it segments 60 anatomical structures on unseen research datasets and real-world X-rays. The authors release the model, code, a full-body segmentation dataset, and a local browser tool.
- A physics-based engine simulates annotated X-rays from whole-body CT.
- FleXray segments 60 structures on unseen and real-world radiographs.
- Supports disease grading, intervention navigation, and pathology learning.
- Model, code, dataset, and a browser tool are publicly released.
Full article193 words · extracted from arxiv.org · click to collapse
X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D projection, causing structures to overlap and anatomical boundaries to be ambiguous, even to experts. As a result, labeling X-ray databases for training general-purpose segmentation systems is impractical, leaving morphometric and functional X-ray analysis confined to narrow anatomical regions and applications. To this end, we present FleXray, a generalist model for anatomical segmentation across the entire body in clinical X-rays. Instead of curating large, manually annotated X-ray datasets, we build a scalable, physics-based generative X-ray data engine. Using existing 3D whole-body CT segmentation datasets and generative image-editing models, we simulate fully-annotated 2D X-rays with diverse appearances, physiological properties, and imaging geometries. Trained on these simulations, FleXray accurately segments 60 anatomical structures across unseen research datasets and in-the-wild X-rays. We further show that FleXray makes X-rays directly amenable to quantitative analysis, enabling automated measurements for disease grading, robust navigation during X-ray-guided interventions, and data-efficient learning of pathological targets. We release the model, code, a full-body X-ray segmentation dataset, and a local, easy-to-use browser-based tool at https://flexray.csail.mit.edu .
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.26756