8 citations · 28 across the 16 of their papers we have counts for
5 papers · 1 filter
Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)
Qi Zeng, Weide Liu, Bo Li +3
This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require lar…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time
Haykel Snoussi, Davood Karimi
Early and accurate assessment of brain microstructure using diffusion Magnetic Resonance Imaging (dMRI) is crucial for identifying neurodevelopmental disorders in neonates, but rem…
Streamline tractography of the fetal brain in utero with machine learning
Weide Liu, Camilo Calixto, Simon K. Warfield +1
Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely…
Anatomically Constrained Tractography of the Fetal Brain
Camilo Calixto, Camilo Jaimes, Matheus D. Soldatelli +3
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, wh…