Archived
2026-09-23FleXray: Universal Clinical X-ray Segmentation
Victor Ion Butoi, Vivek Gopalakrishnan, John V. Guttag, Adrian V. Dalca, Neel Dey
arXiv:2609.26756v1X-ray is one of medicine’s most common imaging tools, but it’s also one of the hardest to analyze quantitatively because everything is flattened into a single 2D image and structures overlap. This paper tackles that problem by building FleXray, a general-purpose segmentation model that can identify anatomical structures across the whole body in clinical X-rays. The key idea is surprisingly practical: instead of collecting huge amounts of hand-labeled X-ray data, the authors generate realistic synthetic X-rays from existing 3D CT segmentation datasets and image-editing models, so they can train with dense labels at scale. The result is a model that segments 60 anatomical structures, even on unseen datasets and real-world images. That matters because segmentation is the foundation for measurement, diagnosis support, and image-guided procedures. In short, FleXray turns ordinary X-rays from a mostly qualitative image into a quantitative tool, and it does so with a data strategy that could make similar medical AI systems much easier to build.
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