# A Deep Generative Model of Neonatal Cortical Surface Development

arXiv: https://arxiv.org/abs/2206.07542 · HTML: https://mlsanity.com/paper/2206.07542/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 24 of 24)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)](https://mlsanity.com/paper/1808.04256/) (2018)
- [MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction](https://mlsanity.com/paper/2007.13559/) (2020)
- [Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size](https://mlsanity.com/paper/1803.06629/) (2018)
- [Learning to synthesise the ageing brain without longitudinal data](https://mlsanity.com/paper/1912.02620/) (2019)
- [Spherical U-Net on Cortical Surfaces: Methods and Applications](https://mlsanity.com/paper/1904.00906/) (2019)
- [Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN](https://mlsanity.com/paper/1809.04536/) (2018)
- [The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN](https://mlsanity.com/paper/1910.13681/) (2019)
- [Vox2Vox: 3D-GAN for Brain Tumour Segmentation](https://mlsanity.com/paper/2003.13653/) (2020)
- [ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping](https://mlsanity.com/paper/2006.08287/) (2020)
- [Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT](https://mlsanity.com/paper/1806.07777/) (2018)
- [ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans](https://mlsanity.com/paper/2103.02561/) (2021)
- [Adversarial Image Registration with Application for MR and TRUS Image Fusion](https://mlsanity.com/paper/1804.11024/) (2018)
- [Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity](https://mlsanity.com/paper/2109.03115/) (2021)
- [FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution](https://mlsanity.com/paper/2108.03920/) (2021)
- [Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks](https://mlsanity.com/paper/1811.09750/) (2018)
- [DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet](https://mlsanity.com/paper/1902.06085/) (2019)
- [Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface](https://mlsanity.com/paper/2008.06098/) (2020)
- [Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs](https://mlsanity.com/paper/1809.06276/) (2018)

## Built on by (most cited first, 1 of 1)

- [Cortical Surface Diffusion Generative Models](https://mlsanity.com/paper/2402.04753/) (2024)

## Abstract (arXiv)

The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional filters are inappropriate on non-flat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate sphericalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between different stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation (> 37 weeks gestation), validated through comparison with a trained term/preterm classifier. Simulated differences in cortical maturation are consistent with observations in the literature.