63 citations · 291 across the 50 of their papers we have counts for
51 papers
PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +14
Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
From 100,000+ images to winning the first brain MRI foundation model challenges: Sharing lessons and models
Pedro M. Gordaliza, Jaume Banus, Benoît Gérin +4
Developing Foundation Models for medical image analysis is essential to overcome the unique challenges of radiological tasks. The first challenges of this kind for 3D brain MRI, SS…
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…
Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis
Thomas Sanchez, Gerard Martí-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…
Accounting for Underspecification in Statistical Claims of Model Superiority
Thomas Sanchez, Pedro M. Gordaliza, Meritxell Bach Cuadra
Machine learning methods are increasingly applied in medical imaging, yet many reported improvements lack statistical robustness: recent works have highlighted that small but signi…
T2 mapping at 0.55 T using Ultra-Fast Spin Echo MRI
Margaux Roulet, Hamza Kebiri, Busra Bulut +9
Low-field T2 mapping MRI can democratize neuropediatric imaging by improving accessibility and providing quantitative biomarkers of brain development. \textbf{Purpose:} To evaluate…
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,…
Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization
Marina Grifell i Plana, Vladyslav Zalevskyi, Léa Schmidt +6
Accurate fetal brain segmentation is crucial for extracting biomarkers and assessing neurodevelopment, especially in conditions such as corpus callosum dysgenesis (CCD), which can…
Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +9
T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is cha…
ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions
Maxence Wynen, Pedro M. Gordaliza, Maxime Istasse +4
Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely o…
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…
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…
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
Ziyao Shang, Misha Kaandorp, Kelly Payette +9
Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis…
Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study
Tommaso Di Noto, Sofyan Jankowski, Francesco Puccinelli +9
Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess…
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…
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…
Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains
Rizhong Lin, Hamza Kebiri, Ali Gholipour +4
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representat…
Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza +8
Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresse…
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Federico Spagnolo, Nataliia Molchanova, Meritxell Bach Cuadra +5
Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-…
In-vivo imaging of the human thalamus: a comprehensive evaluation of structural magnetic resonance imaging approaches for thalamic nuclei differentiation at 7T
Cristina Sainz Martinez, José P. Marques, Gabriele Bonanno +4
The thalamus is a subcortical structure of central importance to brain function, which is organized in smaller nuclei with specialized roles. Despite significant functional and cli…
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +4
Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment. However, automated tools face substantial d…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
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 th…
Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains
Rizhong Lin, Ali Gholipour, Jean-Philippe Thiran +3
Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift ch…