References¶

This dataset and benchmark are based on the following references:

• Fawaz, A. et al. (2021). Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction. bioRxiv.
• Dahan, S. et al. (2022). Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis. MIDL

The following reference the background and motivation for this challenge:¶

• Glasser, M. F. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
• Kong, R. et al. (2019). Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cerebral cortex, 29(6), 2533-2551.

Below are the primary references for dHCP data acquisition and processing pipelines:

• Hughes, E. J. et al. (2017). A dedicated neonatal brain imaging system. Magnetic resonance in medicine, 78(2), 794-804.
• Makropoulos, A. et al. (2018). The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage, 173, 88-112.
• Bozek J. et al. (2018). Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project. NeuroImage, 179, 11-29.
• Cordero‐Grande, L. et al. (2018). Three‐dimensional motion corrected sensitivity encoding reconstruction for multi‐shot multi‐slice MRI: application to neonatal brain imaging. Magnetic resonance in medicine, 79(3), 1365-1376.
• Kuklisova-Murgasova, M. et al. (2012). Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Medical image analysis, 16(8), 1550-1564.