The prenatal period is increasingly considered as a crucial target for the primary prevention
of neurodevelopmental and psychiatric disorders. Understanding their pathophysiological …

Brain segmentation is often the first and most critical step in quantitative analysis of the brain
for many clinical applications, including fetal imaging. Different aspects challenge the …

Despite the state-of-the-art performance for medical image segmentation, deep
convolutional neural networks (CNNs) have rarely provided uncertainty estimations …

High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices
plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies …

Brain extraction or whole brain segmentation is an important first step in many of the
neuroimage analysis pipelines. The accuracy and the robustness of brain extraction …

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal
MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus …

In this study, we developed an automated workflow using a deep learning model (DL) to
measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified …

Reconstructing a high-resolution (HR) volume from motion-corrupted and sparsely acquired
stacks plays an increasing role in fetal brain Magnetic Resonance Imaging (MRI) studies …

Brain extraction (masking of extra-cerebral tissues) and alignment are fundamental first
steps of most neuroimage analysis pipelines. The lack of automated solutions for 3D …

Purpose To improve motion robustness of functional fetal MRI scans by developing an
intrinsic real‐time motion correction method. MRI provides an ideal tool to characterize fetal …