Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Deep learning approaches, especially convolutional neural networks (CNNs), have become
the method of choice in the field of medical image analysis over the last few years. This …

Many anatomical structures can be described by surface or volume meshes. Machine
learning is a promising tool to extract information from these 3D models. However, high …

Cortical surface registration is a fundamental tool for neuroimaging analysis that has been
shown to improve the alignment of functional regions relative to volumetric approaches …

Convolutional neural networks (CNN) have been broadly studied on images, videos,
graphs, and triangular meshes. However, it has seldom been studied on tetrahedral …

Abstract The Human Connectome Project Multimodal Parcellation (HCP_MMP1. 0) provides
a robust in vivo map of the cerebral cortex, which demonstrates variability in structure and …

Surface meshes are a favoured domain for representing structural and functional information
on the human cortex, but their complex topology and geometry pose significant challenges …

Brain anatomical age is an effective feature to assess the status of the brain, such as atypical
development and aging. Although some deep learning models have been developed for …

Cortical neurodevelopment is sensitive to disruption following preterm birth, with lasting
impact on cognitive outcomes. The creation of generative models of neurodevelopment …

This paper introduces GeoMorph, a novel geometric deep-learning framework designed for
image registration of cortical surfaces. The registration process consists of two main steps …