AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Read the original on arXiv Computer Vision →AnyCXR is a unified framework that performs multi-organ segmentation on chest X-rays taken at any projection angle using only synthetic data. It employs a Multi-stage Domain Randomization engine to generate over 100,000 anatomically accurate synthetic radiographs from 3D CT volumes, and a Conditional Joint Annotation Regularization strategy to learn from partial, imperfect labels by enforcing anatomical consistency in a latent space. Trained solely on synthetic data, AnyCXR achieves strong zero-shot generalization on real-world datasets, accurately delineating 54 anatomical structures across PA, lateral, and oblique views, and supports downstream clinical tasks such as cardiothoracic ratio estimation, spine curvature assessment, and disease classification.
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