15 citations · 34 across the 6 of their papers we have counts for
6 papers
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov +7
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions a…
FetMRQC: a robust quality control system for multi-centric fetal brain MRI
Thomas Sanchez, Oscar Esteban, Yvan Gomez +8
Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout ge…
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Valentin Comte, Mireia Alenya, Andrea Urru +10
Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditiona…
FetMRQC: Automated Quality Control for fetal brain MRI
Thomas Sanchez, Oscar Esteban, Yvan Gomez +2
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where large and unpr…
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
Carla Sendra-Balcells, Víctor M. Campello, Jordina Torrents-Barrena +17
Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower…
An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
Andrea Urru, Ayako Nakaki, Oualid Benkarim +9
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…