FleXray: Universal Clinical X-ray Segmentation

2026-09-22

AI Summary

为解决X射线成像中结构重叠和边界模糊问题，本文提出FleXray模型，通过基于物理的生成式数据引擎模拟全标注2D X射线图像进行训练，实现全身解剖结构分割。

Abstract

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 .

Problem

• X-ray
• segmentation
• anatomical structures
• quantitative analysis
• data engine

Innovation

• Universal Clinical X-ray Segmentation
• Physics-based Generative X-ray Data Engine
• Automated Measurements for Disease Grading