Gerard Marti-Juan

@upf.edu

Department of Information and Communication Technologies, BCN Medtech
Universitat Pompeu Fabra

Gerard Marti-Juan


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https://researchid.co/gerardmjuan

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Radiology, Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, General Neuroscience
12

Scopus Publications

274

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Advances in automated fetal brain MRI segmentation and biometry: Insights from the FeTA 2024 challenge
    Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp, Margaux Roulet, Diego Fajardo-Rojas, Liu Li, Jana Hutter, Hongwei Bran Li, Matthew J. Barkovich, Hui Ji, Luca Wilhelmi, Aline Dändliker, Céline Steger, Mériam Koob, Yvan Gomez, Anton Jakovčić, Melita Klaić, Ana Adžić, Pavel Marković, Gracia Grabarić, Milan Rados, Jordina Aviles Verdera, Gregor Kasprian, Gregor Dovjak, Raphael Gaubert-Rachmühl, Maurice Aschwanden, Qi Zeng, Davood Karimi, Denis Peruzzo, Tommaso Ciceri, Giorgio Longari, Rachika E. Hamadache, Amina Bouzid, Xavier Lladó, Simone Chiarella, Gerard Martí-Juan, Miguel Ángel González Ballester, Marco Castellaro, Marco Pinamonti, Valentina Visani, Robin Cremese, Keïn Sam, Fleur Gaudfernau, Param Ahir, Mehul Parikh, Maximilian Zenk, Michael Baumgartner, Klaus Maier-Hein, Li Tianhong, Yang Hong, Zhao Longfei, Domen Preloznik, Žiga Špiclin, Jae Won Choi, Muyang Li, Jia Fu, Guotai Wang, Jingwen Jiang, Lyuyang Tong, Bo Du, Andrea Gondova, Sungmin You, Kiho Im, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Andras Jakab, Roxane Licandro, Kelly Payette, Meritxell Bach Cuadra
    Medical Image Analysis, 2026
    Accurate fetal brain tissue segmentation and biometric measurement are essential for monitoring neurodevelopment and detecting abnormalities in utero. The Fetal Tissue Annotation (FeTA) Challenges have established robust multi-center benchmarks for evaluating state-of-the-art segmentation methods. This paper presents the results of the 2024 challenge edition, which introduced three key innovations. First, we introduced a topology-aware metric based on the Euler characteristic difference (ED) to overcome the performance plateau observed with traditional metrics like Dice or Hausdorff distance (HD), as the performance of the best models in segmentation surpassed the inter-rater variability. While the best teams reached similar scores in Dice (0.81-0.82) and HD95 (2.1-2.3 mm), ED provided greater discriminative power: the winning method achieved an ED of 20.9, representing roughly a 50% improvement over the second- and third-ranked teams despite comparable Dice scores. Second, we introduced a new 0.55T low-field MRI test set, which, when paired with high-quality super-resolution reconstruction, achieved the highest segmentation performance across all test cohorts (Dice=0.86, HD95=1.69, ED=6.26). This provides the first quantitative evidence that low-cost, low-field MRI can match or surpass high-field systems in automated fetal brain segmentation. Third, the new biometry estimation task exposed a clear performance gap: although the best model reached a mean average percentage error (MAPE) of 7.72%, most submissions failed to outperform a simple gestational-age-based linear regression model (MAPE=9.56%), and all remained above inter-rater variability with a MAPE of 5.38%. Finally, by analyzing the top-performing models from FeTA 2024 alongside those from previous challenge editions, we identify ensembles of 3D nnU-Net trained on both real and synthetic data with both image- and anatomy-level augmentations as the most effective approaches for fetal brain segmentation. Our quantitative analysis reveals that acquisition site, super-resolution strategy, and image quality are the primary sources of domain shift, informing recommendations to enhance the robustness and generalizability of automated fetal brain analysis methods.
  • Automatic Quality Assurance and Subcortical Brain Segmentation in Pediatric Ultra-Low-Field MRI: Exploring Ordinal Learning and Foundation Model Adaptation
    Raquel González López, Maria Chiara Fiorentino, Gerard Martí-Juan, Oscar Camara, Miguel A. González Ballester
    Lecture Notes in Computer Science, 2026
    Ultra-low-field (uLF) MRI systems offer portable and affordable neuroimaging solutions for pediatric patients and are valuable in resource-limited settings. However, such systems are susceptible to poor image quality, artifacts, and low contrast, making brain segmentation difficult. This study addresses two critical challenges in uLF MRI: automated quality assessment (QA) and anatomical structure segmentation. For QA, we propose a multi-label approach that incorporates the ordinal nature of artifact severity through an ordinal loss and models artifact co-occurrence patterns using Bayesian Networks. The approach is enhanced through aggressive synthetic data augmentation and ensemble learning, achieving a composite accuracy score of 0.84 across seven artifact categories. For segmentation, we benchmark a task-specific model (nnU-net) against a foundation model (SAM-Med3D) on the delineation of challenging subcortical structures. While nnU-Net, trained from scratch, achieved mean Dice score of 0.72 for hippocampi and 0.86 for basal ganglia, we demonstrate that lightweight fine-tuning of SAM-Med3D yields comparable results with a mean Dice score of 0.70 in hippocampi segmentation, despite domain shift. These results underscore the promise of foundation models for medical imaging in low-resource contexts, while highlighting the importance of domain adaptation. Overall, our pipeline represents a step forward in robust, automated QA and segmentation in uLF MRI for pediatric use. We release the code at https://github.com/reitxel/LISA2025TeamUPF .
  • Automatic Quality Control in Multi-centric Fetal Brain MRI Super-Resolution Reconstruction
    Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov, Gerard Martí Juan, Elisenda Eixarch, Andras Jakab, Vincent Dunet, Mériam Koob, Guillaume Auzias, Meritxell Bach Cuadra
    Lecture Notes in Computer Science, 2026
  • Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction
    Thomas Sanchez, Angeline Mihailov, Mériam Koob, Nadine Girard, Aurélie Manchon, Ignacio Valenzuela, Marta Gómez-Chiari, Gerard Martí Juan, Alexandre Pron, Elisenda Eixarch, Gemma Piella, Miguel Angel Gonzalez Ballester, Oscar Camara, Vincent Dunet, Guillaume Auzias, Meritxell Bach Cuadra
    Pediatric Radiology, 2025
    Background Fetal brain MRI is increasingly used to complement ultrasound imaging. Images are processed using complex super-resolution reconstruction pipelines, which may bias biometric and volumetric measurements. Objective To assess the consistency of 2-dimensional (D) biometric and 3-D volumetric measurements across three hospitals using three widely used super-resolution reconstruction pipelines. Materials and methods This retrospective multi-centric study used T2-weighted fetal brain MRI scans acquired at three hospitals between 2009 and 2023. MRIs from each subject were reconstructed with each super-resolution reconstruction method, and biometric measurements were performed by four experts. Automated 3-D volumetry was performed using a state-of-the-art segmentation method. A qualitative evaluation assessed the clinicians’ likelihood of using super-resolution reconstructed volumes in their practice. Results Eighty-four healthy subjects were included. Biometric measurements revealed statistically significant changes that consistently remained below voxel width (0.8 mm; P<0.001). Automated 3-D volumetry revealed small systematic effects (<2.8%; P<0.001). The qualitative evaluation showed systematic differences between super-resolution reconstruction methods for the perception of white matter intensity (P=0.02) and sharpness of the image (P=0.01). Conclusion Variations in 2-D and 3-D quantitative measurements did not show any large systematic bias when using different super-resolution reconstruction methods for clinical radiological assessment across centers, scanners, and raters.
  • Longitudinal Assessment of Abnormal Cortical Folding in Fetuses and Neonates With Isolated Non-Severe Ventriculomegaly
    Andrea Urru, Oualid Benkarim, Gerard Martí‐Juan, Nadine Hahner, Gemma Piella, Elisenda Eixarch, Miguel A. González Ballester
    Brain and Behavior, 2025
    PurposeThe impact of ventriculomegaly (VM) on cortical development and brain functionality has been extensively explored in existing literature. VM has been associated with higher risks of attention‐deficit and hyperactivity disorders, as well as cognitive, language, and behavior deficits. Some studies have also shown a relationship between VM and cortical overgrowth, along with reduced cortical folding, both in fetuses and neonates. However, there is a lack of longitudinal studies that study this relationship from fetuses to neonates.MethodWe used a longitudinal dataset of 30 subjects (15 healthy controls and 15 subjects diagnosed with isolated non‐severe VM (INSVM)) with structural MRI acquired in and ex utero for each subject. We focused on the impact of fetal INSVM on cortical development from a longitudinal perspective, from the fetal to the neonatal stage. Particularly, we examined the relationship between ventricular enlargement and both volumetric features and a multifaceted set of cortical folding measures, including local gyrification, sulcal depth, curvature, and cortical thickness.FindingsOur results show significant effects of isolated non‐severe VM (INSVM) compared to healthy controls, with reduced cortical thickness in specific brain regions such as the occipital, parietal, and frontal lobes.ConclusionThese findings align with existing literature, confirming the presence of alterations in cortical growth and folding in subjects with isolated non‐severe VM (INSVM) from the fetal to neonatal stage compared to controls.
  • Conservation of structural brain connectivity in people with multiple sclerosis
    Gerard Martí-Juan, Jaume Sastre-Garriga, Angela Vidal-Jordana, Sara Llufriu, Eloy Martinez-Heras, Sergiu Groppa, Gabriel González-Escamilla, Maria A. Rocca, Massimo Filippi, Einar A. Høgestøl, Hanne F. Harbo, Michael A. Foster, Sara Collorone, Ahmed T. Toosy, Menno M. Schoonheim, Eva Strijbis, Giuseppe Pontillo, Maria Petracca, Gustavo Deco, Àlex Rovira, Deborah Pareto
    Network Neuroscience, 2024
    Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity. The aim of this study was to replicate this conservation principle in subjects with MS and to explore how the disease interacts with it. A multicentric dataset has been analyzed including 513 people with MS and 208 healthy controls from seven different centers. Structural connectivity was quantified through various connectivity measures, and graph analysis was used to study the behavior of intra- and interhemispheric connectivity. The association between the intra- and the interhemispheric connectivity showed a similar strength for healthy controls (r = 0.38, p < 0.001) and people with MS (r = 0.35, p < 0.001). Intrahemispheric connectivity was associated with white matter fraction (r = 0.48, p < 0.0001), lesion volume (r = −0.44, p < 0.0001), and the Symbol Digit Modalities Test (r = 0.25, p < 0.0001). Results show that this conservation principle seems to hold for people with MS. These findings support the hypothesis that interhemispheric connectivity decreases at higher cognitive decline and disability levels, while intrahemispheric connectivity increases to maintain the balance.
  • Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study
    Gerard Martí-Juan, Jaume Sastre-Garriga, Eloy Martinez-Heras, Angela Vidal-Jordana, Sara Llufriu, Sergiu Groppa, Gabriel Gonzalez-Escamilla, Maria A Rocca, Massimo Filippi, Einar A Høgestøl, Hanne F Harbo, Michael A Foster, Ahmed T Toosy, Menno M Schoonheim, Prejaas Tewarie, Giuseppe Pontillo, Maria Petracca, Àlex Rovira, Gustavo Deco, Deborah Pareto
    Cerebral Cortex, 2023
    The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC–FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC–FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC–FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.
  • MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer's disease progression modelling
    Gerard Martí-Juan, Marco Lorenzi, Gemma Piella
    Neuroimage, 2023
    The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.
  • Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network
    Gerard Martí-Juan, Marcos Frías, Aran Garcia-Vidal, Angela Vidal-Jordana, Manel Alberich, Willem Calderon, Gemma Piella, Oscar Camara, Xavier Montalban, Jaume Sastre-Garriga, Àlex Rovira, Deborah Pareto
    Neuroimage Clinical, 2022
    BACKGROUND: Optic neuritis (ON) is one of the first manifestations of multiple sclerosis, a disabling disease with rising prevalence. Detecting optic nerve lesions could be a relevant diagnostic marker in patients with multiple sclerosis. OBJECTIVES: We aim to create an automated, interpretable method for optic nerve lesion detection from MRI scans. MATERIALS AND METHODS: We present a 3D convolutional neural network (CNN) model that learns to detect optic nerve lesions based on T2-weighted fat-saturated MRI scans. We validated our system on two different datasets (N = 107 and 62) and interpreted the behaviour of the model using saliency maps. RESULTS: The model showed good performance (68.11% balanced accuracy) that generalizes to unseen data (64.11%). The developed network focuses its attention to the areas that correspond to lesions in the optic nerve. CONCLUSIONS: The method shows robustness and, when using only a single imaging sequence, its performance is not far from diagnosis by trained radiologists with the same constraint. Given its speed and performance, the developed methodology could serve as a first step to develop methods that could be translated into a clinical setting.
  • Nonlinear interaction between APOE ε4 allele load and age in the hippocampal surface of cognitively intact individuals
    Gerard Martí‐Juan, Gerard Sanroma‐Guell, Raffaele Cacciaglia, Carles Falcon, Grégory Operto, José Luis Molinuevo, Miguel Ángel González Ballester, Juan Domingo Gispert, Gemma Piella, , and
    Human Brain Mapping, 2021
    The ε4 allele of the gene Apolipoprotein E is the major genetic risk factor for Alzheimer's Disease. APOE ε4 has been associated with changes in brain structure in cognitively impaired and unimpaired subjects, including atrophy of the hippocampus, which is one of the brain structures that is early affected by AD. In this work we analyzed the impact of APOE ε4 gene dose and its association with age, on hippocampal shape assessed with multivariate surface analysis, in a ε4‐enriched cohort of n = 479 cognitively healthy individuals. Furthermore, we sought to replicate our findings on an independent dataset of n = 969 individuals covering the entire AD spectrum. We segmented the hippocampus of the subjects with a multi‐atlas‐based approach, obtaining high‐dimensional meshes that can be analyzed in a multivariate way. We analyzed the effects of different factors including APOE, sex, and age (in both cohorts) as well as clinical diagnosis on the local 3D hippocampal surface changes. We found specific regions on the hippocampal surface where the effect is modulated by significant APOE ε4 linear and quadratic interactions with age. We compared between APOE and diagnosis effects from both cohorts, finding similarities between APOE ε4 and AD effects on specific regions, and suggesting that age may modulate the effect of APOE ε4 and AD in a similar way.
  • A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease
    Gerard Martí-Juan, Gerard Sanroma-Guell, Gemma Piella
    Computer Methods and Programs in Biomedicine, 2020
  • Revealing heterogeneity of brain imaging phenotypes in Alzheimer’s disease based on unsupervised clustering of blood marker profiles
    Gerard Martí-Juan, Gerard Sanroma, Gemma Piella, and
    Plos One, 2019

RECENT SCHOLAR PUBLICATIONS

  • Data quality biases normative models derived from fetal brain MRI
    T Sanchez, A Mihailov, G Martí-Juan, N Girard, A Manchon, M Milh, ...
    bioRxiv, 2026.01. 22.700996 , 2026
    2026
    Citations: 1
  • Advances in automated fetal brain MRI segmentation and biometry: insights from the feta 2024 challenge
    V Zalevskyi, T Sanchez, M Kaandorp, M Roulet, D Fajardo-Rojas, L Li, ...
    Medical image analysis, 103941 , 2026
    2026
    Citations: 10
  • Fetpype: An open-source pipeline for reproducible fetal brain mri analysis
    T Sanchez, G Martí-Juan, D Meunier, MAG Ballester, O Camara, ...
    arXiv preprint arXiv:2512.17472 , 2025
    2025
    Citations: 2
  • Correction to: Automatic Quality Assurance and Subcortical Brain Segmentation in Pediatric Ultra-Low-Field MRI: Exploring Ordinal Learning and Foundation Model Adaptation
    RG López, MC Fiorentino, G Martí-Juan, O Camara, ...
    MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image … , 2025
    2025
  • Automatic Quality Assurance and Subcortical Brain Segmentation in Pediatric Ultra-Low-Field MRI: Exploring Ordinal Learning and Foundation Model Adaptation
    RG López, MC Fiorentino, G Martí-Juan, O Camara, ...
    MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image … , 2025
    2025
  • Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
    T Sanchez, V Zalevskyi, A Mihailov, G Martí Juan, E Eixarch, A Jakab, ...
    International Workshop on Preterm, Perinatal and Paediatric Image Analysis, 3-14 , 2025
    2025
    Citations: 5
  • Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction
    T Sanchez, A Mihailov, M Koob, N Girard, A Manchon, I Valenzuela, ...
    Pediatric Radiology 55 (10), 2064-2075 , 2025
    2025
    Citations: 4
  • Longitudinal Assessment of Abnormal Cortical Folding in Fetuses and Neonates With Isolated Non‐Severe Ventriculomegaly
    A Urru, O Benkarim, G Martí‐Juan, N Hahner, G Piella, E Eixarch, ...
    Brain and Behavior 15 (1), e70255 , 2025
    2025
    Citations: 3
  • Conservation of structural brain connectivity in people with multiple sclerosis
    G Martí-Juan, J Sastre-Garriga, A Vidal-Jordana, S Llufriu, ...
    Network Neuroscience 8 (4), 1545-1562 , 2024
    2024
    Citations: 1
  • Assessing data quality on fetal brain MRI reconstruction: a multi-site and multi-rater study
    T Sanchez, A Mihailov, Y Gomez, GM Juan, E Eixarch, A Jakab, V Dunet, ...
    International Workshop on Preterm, Perinatal and Paediatric Image Analysis … , 2024
    2024
    Citations: 10
  • Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study
    G Martí-Juan, J Sastre-Garriga, E Martinez-Heras, A Vidal-Jordana, ...
    Cerebral Cortex 33 (12), 7322-7334 , 2023
    2023
    Citations: 16
  • MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
    G Marti-Juan, M Lorenzi, G Piella, ...
    NeuroImage 268, 119892 , 2023
    2023
    Citations: 46
  • Investigating the balance between inter-and intrahemispheric connectivity brain structural connectivity in multiple sclerosis. Validation in a multi-center study
    G Marti-Juan, J Sastre-Garriga, A Vidal-Jordana, E De Las Heras, ...
    MULTIPLE SCLEROSIS 29 (3S), 11-13 , 2023
    2023
  • Using The Virtual Brain to study the relationship between structural and functional connectivity in people with multiple sclerosis: a multicentre study
    G Marti-Juan, J Sastre-Garriga, A Vidal-Jordana, S Llufriu, ...
    MULTIPLE SCLEROSIS 28 (3_SUPPL), 262-264 , 2022
    2022
    Citations: 3
  • Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network
    G Martí-Juan, M Frías, A Garcia-Vidal, A Vidal-Jordana, M Alberich, ...
    NeuroImage: Clinical 36, 103187 , 2022
    2022
    Citations: 10
  • Data-driven methods to characterize heterogeneity in Alzheimer’s disease using cross-sectional and longitudinal data
    G Martí Juan
    Universitat Pompeu Fabra , 2021
    2021
    Citations: 1
  • Nonlinear interaction between APOE ε 4 allele load and age in the hippocampal surface of cognitively intact individuals
    G Martí‐Juan, G Sanroma‐Guell, R Cacciaglia, C Falcon, G Operto, ...
    Human Brain Mapping 42 (1), 47-64 , 2021
    2021
    Citations: 15
  • A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s Disease
    G Martí-Juan, G Sanroma-Guell, G Piella
    Computer Methods and Programs in Biomedicine, 105348 , 2020
    2020
    Citations: 108
  • Revealing heterogeneity of brain imaging phenotypes in Alzheimer’s disease based on unsupervised clustering of blood marker profiles
    G Martí-Juan, G Sanroma, G Piella, ...
    PloS one 14 (3), e0211121 , 2019
    2019
    Citations: 18
  • Towards large scale multimedia indexing: A case study on person discovery in broadcast news
    N Le, H Bredin, G Sargent, M India, P Lopez-Otero, C Barras, ...
    Proceedings of the 15th International Workshop on Content-Based Multimedia … , 2017
    2017
    Citations: 16

MOST CITED SCHOLAR PUBLICATIONS

  • A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s Disease
    G Martí-Juan, G Sanroma-Guell, G Piella
    Computer Methods and Programs in Biomedicine, 105348 , 2020
    2020
    Citations: 108
  • MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
    G Marti-Juan, M Lorenzi, G Piella, ...
    NeuroImage 268, 119892 , 2023
    2023
    Citations: 46
  • Revealing heterogeneity of brain imaging phenotypes in Alzheimer’s disease based on unsupervised clustering of blood marker profiles
    G Martí-Juan, G Sanroma, G Piella, ...
    PloS one 14 (3), e0211121 , 2019
    2019
    Citations: 18
  • Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study
    G Martí-Juan, J Sastre-Garriga, E Martinez-Heras, A Vidal-Jordana, ...
    Cerebral Cortex 33 (12), 7322-7334 , 2023
    2023
    Citations: 16
  • Towards large scale multimedia indexing: A case study on person discovery in broadcast news
    N Le, H Bredin, G Sargent, M India, P Lopez-Otero, C Barras, ...
    Proceedings of the 15th International Workshop on Content-Based Multimedia … , 2017
    2017
    Citations: 16
  • Nonlinear interaction between APOE ε 4 allele load and age in the hippocampal surface of cognitively intact individuals
    G Martí‐Juan, G Sanroma‐Guell, R Cacciaglia, C Falcon, G Operto, ...
    Human Brain Mapping 42 (1), 47-64 , 2021
    2021
    Citations: 15
  • Advances in automated fetal brain MRI segmentation and biometry: insights from the feta 2024 challenge
    V Zalevskyi, T Sanchez, M Kaandorp, M Roulet, D Fajardo-Rojas, L Li, ...
    Medical image analysis, 103941 , 2026
    2026
    Citations: 10
  • Assessing data quality on fetal brain MRI reconstruction: a multi-site and multi-rater study
    T Sanchez, A Mihailov, Y Gomez, GM Juan, E Eixarch, A Jakab, V Dunet, ...
    International Workshop on Preterm, Perinatal and Paediatric Image Analysis … , 2024
    2024
    Citations: 10
  • Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network
    G Martí-Juan, M Frías, A Garcia-Vidal, A Vidal-Jordana, M Alberich, ...
    NeuroImage: Clinical 36, 103187 , 2022
    2022
    Citations: 10
  • Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
    T Sanchez, V Zalevskyi, A Mihailov, G Martí Juan, E Eixarch, A Jakab, ...
    International Workshop on Preterm, Perinatal and Paediatric Image Analysis, 3-14 , 2025
    2025
    Citations: 5
  • UPC system for the 2016 MediaEval multimodal person discovery in broadcast TV task
    M India Massana, G Martí-Juan, C Cortillas, G Morros, Bouritsas, E Sayrol, ...
    MediaEval 2016 Multimedia Benchmark Workshop , 2016
    2016
    Citations: 5
  • Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction
    T Sanchez, A Mihailov, M Koob, N Girard, A Manchon, I Valenzuela, ...
    Pediatric Radiology 55 (10), 2064-2075 , 2025
    2025
    Citations: 4
  • Longitudinal Assessment of Abnormal Cortical Folding in Fetuses and Neonates With Isolated Non‐Severe Ventriculomegaly
    A Urru, O Benkarim, G Martí‐Juan, N Hahner, G Piella, E Eixarch, ...
    Brain and Behavior 15 (1), e70255 , 2025
    2025
    Citations: 3
  • Using The Virtual Brain to study the relationship between structural and functional connectivity in people with multiple sclerosis: a multicentre study
    G Marti-Juan, J Sastre-Garriga, A Vidal-Jordana, S Llufriu, ...
    MULTIPLE SCLEROSIS 28 (3_SUPPL), 262-264 , 2022
    2022
    Citations: 3
  • Fetpype: An open-source pipeline for reproducible fetal brain mri analysis
    T Sanchez, G Martí-Juan, D Meunier, MAG Ballester, O Camara, ...
    arXiv preprint arXiv:2512.17472 , 2025
    2025
    Citations: 2
  • Data quality biases normative models derived from fetal brain MRI
    T Sanchez, A Mihailov, G Martí-Juan, N Girard, A Manchon, M Milh, ...
    bioRxiv, 2026.01. 22.700996 , 2026
    2026
    Citations: 1
  • Conservation of structural brain connectivity in people with multiple sclerosis
    G Martí-Juan, J Sastre-Garriga, A Vidal-Jordana, S Llufriu, ...
    Network Neuroscience 8 (4), 1545-1562 , 2024
    2024
    Citations: 1
  • Data-driven methods to characterize heterogeneity in Alzheimer’s disease using cross-sectional and longitudinal data
    G Martí Juan
    Universitat Pompeu Fabra , 2021
    2021
    Citations: 1
  • Correction to: Automatic Quality Assurance and Subcortical Brain Segmentation in Pediatric Ultra-Low-Field MRI: Exploring Ordinal Learning and Foundation Model Adaptation
    RG López, MC Fiorentino, G Martí-Juan, O Camara, ...
    MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image … , 2025
    2025
  • Automatic Quality Assurance and Subcortical Brain Segmentation in Pediatric Ultra-Low-Field MRI: Exploring Ordinal Learning and Foundation Model Adaptation
    RG López, MC Fiorentino, G Martí-Juan, O Camara, ...
    MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image … , 2025
    2025