Chest X-ray HybridGNet Segmentation.

Demo of the HybridGNet model introduced in "Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis."

Instructions:

  1. Upload a chest X-ray image (PA or AP) in PNG or JPEG format.
  2. Click on "Segment Image".

Note: Pre-processing is not needed, it will be done automatically and removed after the segmentation.

Please check citations below.

Examples

If you use this code, please cite:

@article{gaggion2022TMI,
    doi = {10.1109/tmi.2022.3224660},
    url = {https://doi.org/10.1109%2Ftmi.2022.3224660},
    year = 2022,
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    author = {Nicolas Gaggion and Lucas Mansilla and Candelaria Mosquera and Diego H. Milone and Enzo Ferrante},
    title = {Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis},
    journal = {{IEEE} Transactions on Medical Imaging}
}

This model was trained following the procedure explained on:

@misc{gaggion2022ISBI,
title={Multi-center anatomical segmentation with heterogeneous labels via landmark-based models}, 
author={Nicolás Gaggion and Maria Vakalopoulou and Diego H. Milone and Enzo Ferrante},
year={2022},
eprint={2211.07395},
archivePrefix={arXiv},
primaryClass={eess.IV}
}

Example images extracted from Wikipedia, released under:

  1. CC0 Universial Public Domain. Source: https://commons.wikimedia.org/wiki/File:Normal_posteroanterior_(PA)_chest_radiograph_(X-ray).jpg
  2. Creative Commons Attribution-Share Alike 4.0 International. Source: https://commons.wikimedia.org/wiki/File:Chest_X-ray.jpg
  3. Creative Commons Attribution 3.0 Unported. Source https://commons.wikimedia.org/wiki/File:Implantable_cardioverter_defibrillator_chest_X-ray.jpg
  4. Creative Commons Attribution-Share Alike 3.0 Unported. Source: https://commons.wikimedia.org/wiki/File:Medical_X-Ray_imaging_PRD06_nevit.jpg

Author: Nicolás Gaggion Website: ngaggion.github.io