In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging,
offering unprecedented opportunities for the diagnosis and treatment of neurological …

The folding of the human brain mostly takes place in utero, making it challenging to study.
After a few pioneer studies looking into it in post-mortem foetal specimen, modern …

Alzheimer's disease affects around one in every nine persons among the elderly population.
Being a neurodegenerative disease, its cure has not been established till date and is …

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 …

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 …

Temporally consistent and accurate registration and parcellation of longitudinal cortical
surfaces is of great importance in studying longitudinal morphological and functional …

Significant changes in brain morphology occur during the third trimester of gestation. The
capability of deep learning in leveraging these morphological features has enhanced the …

Cortical surface parcellation for fetal brains is essential for the understanding of
neurodevelopmental trajectories during gestations with regional analyses of brain structures …

The increasing availability of global observational data has sparked a demand for deep
learning algorithms on spherical grids to enable intelligent analysis at a global scale …

In addition to model accuracy, current neuroimaging studies require more explainable
model outputs to relate brain development, degeneration, or disorders to uncover atypical …