CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images

Understand

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.