Software and Data

Brain Parcellation Survey

In Arslan et al. NeuroImage 2017, we provide a large-scale systematic comparison of more than 30 state-of-the-art connectivity-driven, anatomical, and random parcellation methods. The preprint, as well as the code and data used for the survey are provided at the dedicated webpage.


The Python - Pytorch implementation of the paper:

is available on github at

It proposes a graph learner module combined with graph CNNS for Visual Question Answering.


The Python - Tensorflow implementation of the paper:

Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, Daniel Rueckert: Spectral Graph Convolutions for Population-based Disease Prediction. MICCAI 2017.

is available on github at

It exploits the novel concept of graphs CNNs to perform semi-supervised classification of populations.


The MATLAB implementation of the paper:

Parisot, S., Arslan, S., Passerat-Palmbach, J., Wells III, W.M., Rueckert, D.: Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex. In: Information Processing in Medical Imaging. pp. 600–612. Springer (2015)

is now available on github at

It performs groupwise and single-subject parcellation of the brain’s cortical surface through a spectral clustering approach.

Please note that we are making use of the code from the multi-scale normalised cuts method introduced in

Timothee Cour, Florence Benezit, Jianbo Shi : Spectral Segmentation with Multiscale Graph Decomposition. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2005.


We have released the database used during my PhD for tumour segmentation and analysis. This is a set of 210 FLAIR MRI of different patients suffering from a diffuse Low Grade Glioma. All the images provided were acquired in a clinical setting, with varying quality and evolution of the disease. It results in a very challenging database for image processing tasks.

The complete database can be found at : and figshare (NIFTI format)