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

TL;DR

CheXmask is a large, multi-center dataset of detailed anatomical segmentation masks for chest X-ray images, created to improve AI model training and evaluation in medical imaging.

Contribution

It introduces a comprehensive, high-quality anatomical segmentation dataset from multiple sources, using HybridGNet for consistent annotations and rigorous validation.

Findings

657,566 segmentation masks generated

Expert validation ensures high annotation quality

Dataset facilitates advanced chest X-ray analysis

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/