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

Source: [CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images](<https://www.sota2.com/research/paper/68fa471a1e209131ce92576e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T06:09:11.654Z

Authors: Nicol\\'as Gaggion, Candelaria Mosquera, Lucas Mansilla, Julia Mariel Saidman, Martina Aineseder, Diego H. Milone, Enzo Ferrante

Published: 2023-07-06T21:08:03.000Z

## 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/

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Lung Segmentation on PadChest (five-fold cross-validation)](<https://www.sota2.com/research/sota/lung-segmentation-on-padchest-five-fold-cross-validation.md>): Lung Segmentation; PadChest (five-fold cross-validation); DSC (%): 93.65.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.03293>)

[PDF](<https://arxiv.org/pdf/2307.03293.pdf>)