Accurate segmentation of infant brain magnetic resonance (MR) images into white matter
(WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early …

In recent years, a variety of segmentation methods have been proposed for automatic
delineation of the fetal and neonatal brain MRI. These methods aim to define regions of …

Abstract The Develo** Human Connectome Project (dHCP) seeks to create the first 4-
dimensional connectome of early life. Understanding this connectome in detail may provide …

Deep learning algorithms and in particular convolutional networks have shown tremendous
success in medical image analysis applications, though relatively few methods have been …

Defining anatomically and functionally meaningful parcellation maps on cortical surface
atlases is of great importance in surface‐based neuroimaging analysis. The conventional …

Brain magnetic resonance imaging (MRI) is widely used to assess brain development in
neonates and to diagnose a wide range of neurological diseases in adults. Such studies are …

Investigating the human brain in utero is important for researchers and clinicians seeking to
understand early neurodevelopmental processes. With the advent of fast magnetic …

Neuroimaging of brain diseases plays a crucial role in understanding brain abnormalities
and early diagnosis. Of great importance is the study of brain abnormalities in utero and the …

Abstract Background and Objective The automatic segmentation of perinatal brain structures
in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth …

BACKGROUND AND PURPOSE: Fetuses with isolated nonsevere ventriculomegaly
(INSVM) are at risk of presenting neurodevelopmental delay. However, the currently used …