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Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation
Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +10
Fetal brain tissue segmentation from magnetic resonance imaging (MRI) is crucial for studying neurodevelopment, but remains challenging due to data heterogeneity and limited annota…
Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis
Thomas Sanchez, Gerard MartÃ-Juan, David Meunier +6
Fetal brain magnetic resonance imaging (MRI) is crucial for assessing neurodevelopment in utero. However, fetal MRI analysis remains technically challenging due to fetal motion, lo…
Causal Attribution of Model Performance Gaps in Medical Imaging Under Distribution Shifts
Pedro M. Gordaliza, Nataliia Molchanova, Jaume Banus +2
Deep learning models for medical image segmentation suffer significant performance drops due to distribution shifts, but the causal mechanisms behind these drops remain poorly unde…
Segmenting infant brains across magnetic fields: Domain randomization and annotation curation in ultra-low field MRI
Vladyslav Zalevskyi, Dondu-Busra Bulut, Thomas Sanchez +1
Early identification of neurodevelopmental disorders relies on accurate segmentation of brain structures in infancy, a task complicated by rapid brain growth, poor tissue contrast,…
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…
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations
Maik Dannecker, Thomas Sanchez, Meritxell Bach Cuadra +6
High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjec…