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

Abstract

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/

The CheXmask dataset is publicly available at https://physionet.org/content/chexmask-cxr-segmentation-data/

References in corpus (8)

• A Survey on Deep Learning in Medical Image Analysis
• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• PadChest: A large chest x-ray image dataset with multi-label annotated reports
• Learning Deformable Registration of Medical Images with Anatomical Constraints
• Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
• CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
• Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
• Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding

Cited by in corpus (6)

• Exploring scalable medical image encoders beyond text supervision
• CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
• In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
• RadEdit: stress-testing biomedical vision models via diffusion image editing
• Mask of truth: model sensitivity to unexpected regions of medical images
• Augmenting Chest X-ray Datasets with Non-Expert Annotations