  The development of successful artificial intelligence models for chest X-ray
analysis relies on large, diverse datasets with high-quality annotations. While
several databases of chest X-ray images have been released, most include
disease diagnosis labels but lack detailed pixel-level anatomical segmentation
labels. To address this gap, we introduce an extensive chest X-ray multi-center
segmentation dataset with uniform and fine-grain anatomical annotations for
images coming from five well-known publicly available databases: ChestX-ray8,
Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657,566
segmentation masks. Our methodology utilizes the HybridGNet model to ensure
consistent and high-quality segmentations across all datasets. Rigorous
validation, including expert physician evaluation and automatic quality
control, was conducted to validate the resulting masks. Additionally, we
provide individualized quality indices per mask and an overall quality
estimation per dataset. This dataset serves as a valuable resource for the
broader scientific community, streamlining the development and assessment of
innovative methodologies in chest X-ray analysis. The CheXmask dataset is
publicly available at:
https://physionet.org/content/chexmask-cxr-segmentation-data/
