  Automated X-ray image segmentation would accelerate research and development
in diagnostic and interventional precision medicine. Prior efforts have
contributed task-specific models capable of solving specific image analysis
problems, but the utility of these models is restricted to their particular
task domain, and expanding to broader use requires additional data, labels, and
retraining efforts. Recently, foundation models (FMs) -- machine learning
models trained on large amounts of highly variable data thus enabling broad
applicability -- have emerged as promising tools for automated image analysis.
Existing FMs for medical image analysis focus on scenarios and modalities where
objects are clearly defined by visually apparent boundaries, such as surgical
tool segmentation in endoscopy. X-ray imaging, by contrast, does not generally
offer such clearly delineated boundaries or structure priors. During X-ray
image formation, complex 3D structures are projected in transmission onto the
imaging plane, resulting in overlapping features of varying opacity and shape.
To pave the way toward an FM for comprehensive and automated analysis of
arbitrary medical X-ray images, we develop FluoroSAM, a language-aligned
variant of the Segment-Anything Model, trained from scratch on 1.6M synthetic
X-ray images. FluoroSAM is trained on data including masks for 128 organ types
and 464 non-anatomical objects, such as tools and implants. In real X-ray
images of cadaveric specimens, FluoroSAM is able to segment bony anatomical
structures based on text-only prompting with 0.51 and 0.79 DICE with
point-based refinement, outperforming competing SAM variants for all
structures. FluoroSAM is also capable of zero-shot generalization to segmenting
classes beyond the training set thanks to its language alignment, which we
demonstrate for full lung segmentation on real chest X-rays.
