Comparison Between CNN and GNN Pipelines for Analysing the Brain in Development

Antoine Bourlier, Antoine Bourlier, Elodie Chaillou, Jean-Yves Ramel, Mohamed Slimane

2025

Abstract

In this study, we present a new pipeline designed for the analysis and comparison of non-conventional animal brain models, such as sheep, without relying on neuroanatomical priors. This innovative approach combines an automatic MRI segmentation with graph neural networks (GNNs) to overcome the limitations of traditional methods. Conventional tools often depend on predefined anatomical atlases and are typically limited in their ability to adapt to the unique characteristics of developing brains or non-conventional animal models. By generating regions of interest directly from MR images and constructing a graph representation of the brain, our method eliminates biases associated with predefined templates. Our results show that the GNN-based pipeline is more efficient in terms of accuracy for an age prediction task (63.22%) compared to a classical CNN architecture (59.77%). GNNs offer notable advantages, including improved interpretability and the ability to model complex relational structures within brain data. Overall, our approach provides a promising solution for unbiased, adaptable, and interpretable analysis of brain MRIs, particularly for developing brains and non-conventional animal models.

Paper Citation

in Harvard Style

Bourlier A., Chaillou E., Ramel J. and Slimane M. (2025). Comparison Between CNN and GNN Pipelines for Analysing the Brain in Development. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 475-482. DOI: 10.5220/0013173500003912

in Bibtex Style

@conference{visapp25,
author={Antoine Bourlier and Elodie Chaillou and Jean-Yves Ramel and Mohamed Slimane},
title={Comparison Between CNN and GNN Pipelines for Analysing the Brain in Development},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={475-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013173500003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Comparison Between CNN and GNN Pipelines for Analysing the Brain in Development
SN - 978-989-758-728-3
AU - Bourlier A.
AU - Chaillou E.
AU - Ramel J.
AU - Slimane M.
PY - 2025
SP - 475
EP - 482
DO - 10.5220/0013173500003912
PB - SciTePress