Sonography has been widely used to assess fetal growth and identify fetal anomalies, 1 and
although fetal neurosonography can analyze brain surface anatomy and maturation, 2 it has …

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the
developing human brain. Automatic segmentation of the developing fetal brain is a vital step …

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain
development at this critical stage. Deep learning methods represent the state of the art in …

Deep neural networks have increased the accuracy of automatic segmentation, however
their accuracy depends on the availability of a large number of fully segmented images …

The performance of deep neural networks typically increases with the number of training
images. However, not all images have the same importance towards improved performance …

We present a new method for end-to-end automatic volumetric segmentation of fetal
structures in MRI scans with deep learning networks trained with very few annotated scans …

Deep learning models for medical image segmentation can fail unexpectedly and
spectacularly for pathological cases and images acquired at different centers than training …

Accurate estimation of the age in neonates is useful for measuring neurodevelopmental,
medical, and growth outcomes. In this paper, we propose a novel approach to predict the …

The fetal cortical plate undergoes drastic morphological changes throughout early in utero
development that can be observed using magnetic resonance (MR) imaging. An accurate …

Abstract Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to
assess early brain development. Accurate segmentation of the different brain tissues is a …