FleXray：覆盖全身 60 个解剖结构的 X 光分割模型
FleXray: Universal Clinical X-ray Segmentation
MIT 把 CT 数据加生成模型变成 X 光标注机，训练出能分全身 60 个结构的模型，还开源了本地工具，搞医学影像的可以自己跑跑看。
MIT 团队发布通用 X 光解剖分割模型 FleXray，可分割全身 60 个解剖结构。训练数据不靠人工标注，而是用 3D 全身 CT 分割数据集和生成式图像编辑模型构建物理仿真的 X 光数据引擎。模型在未见过的研究数据集和真实 X 光片上都能准确分割，支持疾病分级自动测量、X 光引导介入的导航，以及病理目标的高效学习。模型、代码、全身 X 光分割数据集和本地浏览器工具已在项目页面开源。
FleXray: Universal Clinical X-ray Segmentation
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 .
