发表机构
MIT CSAIL; Massachusetts General Hospital; Harvard Medical School（麻省理工学院计算机科学与人工智能实验室; 马萨诸塞总医院; 哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FleXray利用物理生成式数据引擎模拟多样X射线，训练通用模型实现全身60个解剖结构的准确分割，并支持定量分析，推动X射线定量化。
AI 中文摘要
X射线是医学中使用最广泛的成像模态，但其定量化程度仍然最低。与CT或MRI等体积模态不同，X射线将3D解剖结构压缩为2D投影，导致结构重叠，解剖边界模糊，即使对专家也是如此。因此，为训练通用分割系统而标注X射线数据库是不切实际的，这使得形态学和功能性X射线分析局限于狭窄的解剖区域和应用。为此，我们提出了FleXray，一种在临床X射线中实现全身解剖分割的通用模型。我们没有整理大规模手动标注的X射线数据集，而是构建了一个可扩展的、基于物理的生成式X射线数据引擎。利用现有的3D全身CT分割数据集和生成式图像编辑模型，我们模拟了具有多样化外观、生理特性和成像几何的完全标注的2D X射线。在这些模拟上训练后，FleXray在未见过的研究数据集和野外X射线中准确分割了60个解剖结构。我们进一步表明，FleXray使X射线直接适用于定量分析，能够实现疾病分级的自动化测量、X射线引导干预期间的稳健导航，以及病理目标的数据高效学习。我们在此https URL发布了模型、代码、全身X射线分割数据集以及一个本地、易于使用的基于浏览器的工具。
英文摘要
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 .
Comments35 pages, 12 figures, 10 tables. Code, models, data, and a browser-based demo at https://flexray.csail.mit.edu
