Meritxell Bach Cuadra

meritxell.bach@epfl.ch +41 21 693 48 07 https://www.unil.ch/mial/home/menuinst/people/meritxell-bach-cuadra.html

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EPFL STI IEL LTS5
ELD 233 (Bâtiment ELD)
Station 11
1015 Lausanne

+41 21 693 48 07
+41 21 693 35 58
Office: ELD 233
EPFL › STI › IEM › LTS5

Website: https://lts5www.epfl.ch/

EPFL CIBM-SP
CH F0 622 (Bâtiment CH)
Station 6
1015 Lausanne

+41 21 693 48 07
+41 21 693 35 58
Office: ELD 233
EPFL › VPA › VPA-AVP-CP › CIBM › CIBM-SP

Website: https://www.cibm.ch/

Biography Publications Teaching & PhD

Meritxell Bach Cuadra received her degree in Telecommunication Engineering (ETSETB) from the Universitat Politècnica de Catalunya (UPC), Spain, in September 1999. In December 1999, she joined the Computer Vision and Image Analysis Group at the Signal Processing Laboratory (LTS5) as a Ph.D. student under the supervision of Prof. Jean-Philippe Thiran. She obtained her PhD degree in November 2003 for her work "Atlas-based Segmentation and Classification of Magnetic Resonance Brain Images." In June 2005 she joined the Signal Processing Core (SPC) of the Biomedical Imaging Center (CIBM) as responsible of the SPC-CHUV Unit at the Radiology Department of the Lausanne University Hospital (CHUV). As head of the SPC-CHUV unit her goal is to coordinate the medical image analysis research at LTS5 with the needs of expertise in signal/image processing of physicians and researchers at CHUV. In March 2011 Meritxell was promoted to Senior Lecturer (Maître d’Enseignement et de Recherche) at the School of Biology and Medicine of the University of Lausanne where she will teach and develop independent researches at the interface between medical Imaging and image analysis. She is currently affiliated to both the Radiology Department of the Lausanne University Hospital and to the Signal Processing Laboratory LTS5 of the EPFL. Her main research interests are related to quantitative analysis of medical imaging and the integration of prior knowledge in this analysis. Meritxell’s research is focus to: - Segmentation (Active Contours, Graph theory), - Statistical classification (Bayesian framework, Hidden Markov Random Fields) - Image registration - applied to different medical image modalities like Magnetic Resonance (MR) and Diffusion MR imaging, Ultrasound imaging and Computed Tomography.

Trustworthy AI in medical image analysis: A unified perspective built on robustness and layers of trust

M. A. Zuluaga; I. Išgum; M. Bach Cuadra

Current Opinion in Biomedical Engineering. 2026. DOI : 10.1016/j.cobme.2026.100649.

Meritxell Bach Cuadra

Trustworthy AI in medical image analysis: A unified perspective built on robustness and layers of trust

Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg

Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping

Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization

Automatic Quality Control in Multi-centric Fetal Brain MRI Super-Resolution Reconstruction

Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains

A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain

Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI

Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study

Fluid and White Matter Suppression contrasts MRI improves Deep Learning detection of Multiple Sclerosis Cortical Lesions

Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction

Domain shift, domain adaptation, and generalization: A focus on MRI

Assessing Data Quality on Fetal Brain MRI Reconstruction: A Multi-site and Multi-rater Study

A roadmap towards standardized neuroimaging approaches for human thalamic nuclei

Automated Quantitative Susceptibility and Morphometry MR Study: Feasibility and Interrelation Between Clinical Score, Lesion Load, Deep Grey Matter and Normal-Appearing White Matter in Multiple Sclerosis

Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

Streamline RimNet: Tools for Automatic Classification of Paramagnetic Rim Lesions in MRI of Multiple Sclerosis

Toward a joint automated assessment of cortical and paramagnetic rim lesions with 7T MRI

Fluid and White Matter Suppression New Sensitive 3 T Magnetic Resonance Imaging Contrasts for Cortical Lesion Detection in Multiple Sclerosis

A multi-scale probabilistic atlas of the human connectome

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Editorial: Computational Neuroimage Analysis Tools for Brain (Diseases) Biomarkers

Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping

Slice Estimation in Diffusion MRI of Neonatal and Fetal Brains in Image and Spherical Harmonics Domains Using Autoencoders

BMAT: An open-source BIDS managing and analysis tool

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI

An assessment of intra-scanner reproducibility of myelin-sensitive MRI measures

Automated detection of cortical lesions with single and multi-contrast 7T MRI

CLaiMS-Net: cortical lesions assessment in multiple sclerosis patients via a convolutional neural network and a single 3T MRI acquisition

Cortical lesion detection using FLAWS in multiple sclerosis

Chronic White Matter Inflammation and Serum Neurofilament Levels in Multiple Sclerosis

Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging

Automated assessment of paramagnetic rim lesions in multiple sclerosis patients with 3T and 7T MP2RAGE

GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

Quantitative Evaluation of Enhanced Multi-plane Clinical Fetal Diffusion MRI with a Crossing-Fiber Phantom

Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

Model-informed machine learning for multi-component T2 relaxometry

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis

Evolution of Cortical and White Matter Lesion Load in Early-Stage Multiple Sclerosis: Correlation With Neuroaxonal Damage and Clinical Changes

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE

Normalization of aberrant pretherapeutic dynamic functional connectivity of extrastriate visual system in patients who underwent thalamotomy with stereotactic radiosurgery for essential tremor: a resting-state functional MRI study

Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak Labels

Resting-state functional MRI for functional neurosurgery: seeing the light?

Deep learning-based detection of cortical lesions in multiple sclerosis patients with FLAIR, DIR, and MP2RAGE MRI sequences

A fully automated approach for differential diagnosis support in multiple sclerosis based on the central vein sign

Deep learning-based detection of cortical lesions in multiple sclerosis patients with FLAIR, DIR, and MP2RAGE MRI sequences

Axonal damage explains part of and extends beyond the diffuse pathology evidenced by T1 mapping in normal-appearing white matter of multiple sclerosis patients

Deep learning analysis applied to multi-parametric advanced MRI shows higher myelin content and neurite density in juxtacortical lesions compared to periventricular lesions

Axonal and myelin injury in white matter and cortex of relapsing-remitting and progressive multiple sclerosis patients: a combined myelin water and multi-shell diffusion MRI study

Episodic memory decline in Parkinson' s disease: relation with white matter hyperintense lesions and influence of quantification method

Longitudinal analysis of white matter and cortical lesions in multiple sclerosis

Letter: Deep Brain Stimulation of the Pedunculopontine Nucleus Area in Parkinson Disease: Magnetic Resonance Imaging- Based Anatomoclinical Correlations and Optimal Target

Pretherapeutic resting-state fMRI profiles are associated with MR signature volumes after stereotactic radiosurgical thalamotomy for essential tremor

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion-weighted magnetic resonance imaging

Personalized Anatomic Eye Model From T1-Weighted Volume Interpolated Gradient Echo Magnetic Resonance Imaging of Patients With Uveal Melanoma

Automated assessment of new and enlarged white matter and cortical lesions in early multiple sclerosis

Accuracy versus reproducibility: comprehensive assessment of four automated methods for multiple sclerosis lesion segmentation

Differential diagnosis of multiple sclerosis with machine learning-based central vein sign recognition

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

Ocular Structures Segmentation from Multi-sequences MRI Using 3D Unet with Fully Connected CRFs

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

Volume-based vs. voxel-based brain morphometry in Alzheimer's disease prediction

MBIS: Multivariate Bayesian Image Segmentation tool

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

A NEW APPROACH FOR CEREBELLO-THALAMIC MOTOR NETWORK EVALUATION IN ASYMPTOMATIC PREMUTATION CARRIERS AT RISK FOR FXTAS

Multi-atlas fusion methods for segmentation of head and neck lymph nodes for radiotherapy planning

Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

Towards a connectome mapping pipeline for neonates using high-resolution MP2RAGE and DTI

Evaluation of Atlas Fusion Strategies for Segmentation of Head and Neck Lymph Nodes for Radiotherapy Planning