Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical
planning and computer-aided navigation systems. It is challenging due to the high variability …

The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the
cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based …

Accurate reconstruction of cortical surfaces from brain magnetic resonance images (MRIs)
remains a challenging task due to the notorious partial volume effect in brain MRIs and the …

Automated segmentation is a challenging task in medical image analysis that usually
requires a large amount of manually labeled data. However, most current supervised …

Cortical surface reconstruction plays a fundamental role in modeling the rapid brain
development during the perinatal period. In this work, we propose Conditional Temporal …

Statistical shape models (SSMs) are an established way to represent the anatomy of a
population with various clinically relevant applications. However, they typically require …

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and
implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR …

Abstract The Developing Human Connectome Project (dHCP) aims to explore
developmental patterns of the human brain during the perinatal period. An automated …

Synthetic data have emerged as an attractive option for developing machine-learning
methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a …

Convolutional neural networks (CNN) and Transformer variants have emerged as the
leading medical image segmentation backbones. Nonetheless, due to their limitations in …