Visit the Project page.

Implementation of the paper "Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data" (link).

This repository includes:

• The trained model in /trained_model/7im5hf6z/
• A notebook for testing the model on a new data
• A src folder with the code for training a new model

Visit the relative page to learn how to use CEREBRUM-7T from source code, docker, or singularity.

Visit the relative page for all the information needed about the data.

Michele Svanera & Mattia Savardi

If you find this code useful in your research, please consider citing our paper:

Svanera, M., Savardi, M., Signoroni, A., Benini, S., & Muckli, L. (2024). Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data. Medical Image Analysis, 93, 103090. https://doi.org/10.1016/j.media.2024.103090

@article{SVANERA2024103090,
  title = {Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data},
  journal = {Medical Image Analysis},
  volume = {93},
  pages = {103090},
  year = {2024},
  issn = {1361-8415},
  doi = {https://doi.org/10.1016/j.media.2024.103090},
  url = {https://www.sciencedirect.com/science/article/pii/S136184152400015X},
  author = {Michele Svanera and Mattia Savardi and Alberto Signoroni and Sergio Benini and Lars Muckli},
  keywords = {3D segmentation, Brain MRI, Progressive level-of-detail architecture, Multi-site learning},
}