Deep cascaded registration and weakly-supervised segmentation of fetal brain MRI.

Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.

Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, limiting real-world clinical applicability and acceptance.

Effects of Mediterranean diet or mindfulness-based stress reduction on fetal and neonatal brain development: a secondary analysis of a randomized clinical trial.

Background: Maternal suboptimal nutrition and high stress levels are associated with adverse fetal and infant neurodevelopment.

Objective: This study aimed to investigate if structured lifestyle interventions involving a Mediterranean diet or mindfulness-based stress reduction during pregnancy are associated with differences in fetal and neonatal brain development.

Study Design: This was a secondary analysis of the randomized clinical trial Improving Mothers for a Better Prenatal Care Trial Barcelona that was conducted in Barcelona, Spain, from 2017 to 2020.

An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.

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 and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging.

Methods: In this work, a new pipeline for fetal and neonatal segmentation has been developed.