CheXmask Database v1.0.1

A comprehensive collection of anatomical segmentation masks for chest radiographs derived from five major public databases.

Overview

The CheXmask Database provides 657,566 anatomical segmentation masks generated from chest radiographs across multiple public databases:

• ChestX-ray8
• Chexpert
• MIMIC-CXR-JPG
• Padchest
• VinDr-CXR

All segmentation masks were generated using the HybridGNet model and include quality metrics based on Reverse Classification Accuracy (RCA) scores.

Dataset Structure

The dataset consists of CSV files for each source database. Each CSV contains:

Column Name	Description
Image ID	Reference to original image in source dataset
Dice RCA (Max)	Maximum Dice Similarity Coefficient for RCA
Dice RCA (Mean)	Mean Dice Similarity Coefficient for RCA
Landmarks	Organ contour points from HybridGNet model
Left Lung	Left lung segmentation mask in RLE format
Right Lung	Right lung segmentation mask in RLE format
Heart	Heart segmentation mask in RLE format
Height	Height of segmentation mask
Width	Width of segmentation mask

Data Processing

All images were processed to maintain consistent quality:

1. Images were preprocessed to 1024x1024 resolution
2. HybridGNet model was applied for segmentation
3. Masks were restored to original image dimensions
4. RCA scores were calculated for quality assessment

Usage Guidelines

1. Source Images: Users must obtain source images from original databases and comply with their respective requirements (ethics courses, training, etc.).
2. Quality Threshold: For analysis, use only segmentation masks with Dice RCA (Mean) >= 0.7
3. Resolution: Pre-processed versions (1024x1024) of masks are included for consistent resolution across datasets

Version History

v1.0.0

• Updated citation
• Added README file
• Added Data Dictionary

Citation

When using this dataset, please cite: Gaggion, N., Mosquera, C., Mansilla, L. et al. CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images. Sci Data 11, 511 (2024). https://doi.org/10.1038/s41597-024-03358-1