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563 results on '"Eixarch, Elisenda"'

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

Author

Sanchez, Thomas, Mihailov, Angeline, Gomez, Yvan, Juan, Gerard Martí, Eixarch, Elisenda, Jakab, András, Dunet, Vincent, Koob, Mériam, Auzias, Guillaume, Cuadra, Meritxell Bach, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Link-Sourani, Daphna, editor, Abaci Turk, Esra, editor, Macgowan, Christopher, editor, Hutter, Jana, editor, Melbourne, Andrew, editor, and Licandro, Roxane, editor

Published
2025
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2. FetMRQC: a robust quality control system for multi-centric fetal brain MRI

Author

Sanchez, Thomas, Esteban, Oscar, Gomez, Yvan, Pron, Alexandre, Koob, Mériam, Dunet, Vincent, Girard, Nadine, Jakab, Andras, Eixarch, Elisenda, Auzias, Guillaume, and Cuadra, Meritxell Bach

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain., Comment: 24 pages, 10 Figures. Accepted for publication at Medical Image Analysis

Published
2023
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3. Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration

Author

Comte, Valentin, Alenya, Mireia, Urru, Andrea, Recober, Judith, Nakaki, Ayako, Crovetto, Francesca, Camara, Oscar, Gratacós, Eduard, Eixarch, Elisenda, Crispi, Fàtima, Piella, Gemma, Ceresa, Mario, and Ballester, Miguel A. González

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditional deep learning-based automatic segmentation, although effective, requires extensive training data with ground-truth labels, typically produced by clinicians through a time-consuming annotation process. To overcome this challenge, we propose a novel unsupervised segmentation method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image. This cascaded network can then be used to register multiple annotated images with the image to be segmented, and combine the propagated labels to form a refined segmentation. Our experiments demonstrate that the proposed cascaded architecture outperforms the state-of-the-art registration methods that were tested. Furthermore, the derived segmentation method achieves similar performance and inference time to nnU-Net while only using a small subset of annotated data for the multi-atlas segmentation task and none for training the network. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg., Comment: 17 pages, 8 figures, 5 tables, paper submitted to IEEE transaction on medical imaging

Published
2023

4. FetMRQC: Automated Quality Control for fetal brain MRI

Author

Sanchez, Thomas, Esteban, Oscar, Gomez, Yvan, Eixarch, Elisenda, and Cuadra, Meritxell Bach

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

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 unpredictable fetal motion can lead to substantial artifacts in the acquired images. Existing methods for fetal brain quality assessment operate at the \textit{slice} level, and fail to get a comprehensive picture of the quality of an image, that can only be achieved by looking at the \textit{entire} brain volume. In this work, we propose FetMRQC, a machine learning framework for automated image quality assessment tailored to fetal brain MRI, which extracts an ensemble of quality metrics that are then used to predict experts' ratings. Based on the manual ratings of more than 1000 low-resolution stacks acquired across two different institutions, we show that, compared with existing quality metrics, FetMRQC is able to generalize out-of-domain, while being interpretable and data efficient. We also release a novel manual quality rating tool designed to facilitate and optimize quality rating of fetal brain images. Our tool, along with all the code to generate, train and evaluate the model is available at https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc/ ., Comment: 12 pages, 8 figures. Perinatal, Preterm and Paediatric Image Analysis Workshop 2023

Published
2023
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5. Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

Author

Sendra-Balcells, Carla, Campello, Víctor M., Torrents-Barrena, Jordina, Ahmed, Yahya Ali, Elattar, Mustafa, Botwe, Benard Ohene, Nyangulu, Pempho, Stones, William, Ammar, Mohammed, Benamer, Lamya Nawal, Kisembo, Harriet Nalubega, Sereke, Senai Goitom, Wanyonyi, Sikolia Z., Temmerman, Marleen, Gratacós, Eduard, Bonet, Elisenda, Eixarch, Elisenda, Mikolaj, Kamil, Tolsgaard, Martin Grønnebæk, and Lekadir, Karim

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with limited access to high-end ultrasound equipment and data. This work investigates different strategies to reduce the domain-shift effect for a fetal plane classification model trained on a high-resource clinical centre and transferred to a new low-resource centre. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to $0.92\pm0.04$ and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources., Comment: 14 pages, 6 figures, accepted for publication in Scientific Reports

Published
2022

8. An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis

Author

Urru, Andrea, Nakaki, Ayako, Benkarim, Oualid, Crovetto, Francesca, Segales, Laura, Comte, Valentin, Hahner, Nadine, Eixarch, Elisenda, Gratacós, Eduard, Crispi, Fàtima, Piella, Gemma, and Ballester, Miguel A González

Subjects
Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

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 different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, thickness, sulcal depth, and local gyrification index. Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains.

Published
2022

14. Author Correction: Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

Author

Sendra-Balcells, Carla, Campello, Víctor M., Torrents-Barrena, Jordina, Ahmed, Yahya Ali, Elattar, Mustafa, Ohene-Botwe, Benard, Nyangulu, Pempho, Stones, William, Ammar, Mohammed, Benamer, Lamya Nawal, Kisembo, Harriet Nalubega, Sereke, Senai Goitom, Wanyonyi, Sikolia Z., Temmerman, Marleen, Gratacós, Eduard, Bonet, Elisenda, Eixarch, Elisenda, Mikolaj, Kamil, Tolsgaard, Martin Grønnebæk, and Lekadir, Karim

Published
2023
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15. Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

Author

Sendra-Balcells, Carla, Campello, Víctor M., Torrents-Barrena, Jordina, Ahmed, Yahya Ali, Elattar, Mustafa, Ohene-Botwe, Benard, Nyangulu, Pempho, Stones, William, Ammar, Mohammed, Benamer, Lamya Nawal, Kisembo, Harriet Nalubega, Sereke, Senai Goitom, Wanyonyi, Sikolia Z., Temmerman, Marleen, Gratacós, Eduard, Bonet, Elisenda, Eixarch, Elisenda, Mikolaj, Kamil, Tolsgaard, Martin Grønnebæk, and Lekadir, Karim

Published
2023
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17. Effects of Mediterranean diet or mindfulness-based stress reduction on fetal and neonatal brain development: a secondary analysis of a randomized clinical trial

Author

Nakaki, Ayako, Crovetto, Francesca, Urru, Andrea, Piella, Gemma, Borras, Roger, Comte, Valentin, Vellvé, Kilian, Paules, Cristina, Segalés, Laura, Dacal, Marta, Gomez, Yvan, Youssef, Lina, Casas, Rosa, Castro-Barquero, Sara, Martín-Asuero, Andrés, Oller Guzmán, Teresa, Morilla, Ivette, Martínez-Àran, Anabel, Camacho, Alba, Pascual Tutusaus, Mireia, Arranz, Angela, Rebollo-Polo, Monica, Gomez-Chiari, Marta, Bargallo, Nuria, Pozo, Óscar J., Gomez-Gomez, Alex, Izquierdo Renau, Montserrat, Eixarch, Elisenda, Vieta, Eduard, Estruch, Ramon, Crispi, Fàtima, Gonzalez-Ballester, Miguel Angel, and Gratacós, Eduard

Published
2023
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22. Haemodynamic Changes in the Fetal Circulation Post-connection to an Artificial Placenta: A Computational Modelling Study

Author

Villanueva, Maria Inmaculada, López, Marc, Sánchez, Sergio, Garcia-Cañadilla, Patricia, Randanne, Paula C., Hawkins, Ameth, Eixarch, Elisenda, Bonet-Carne, Elisenda, Gratacós, Eduard, Crispi, Fàtima, Bijnens, Bart, Bernardino, Gabriel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Camara, Oscar, editor, Puyol-Antón, Esther, editor, Qin, Chen, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Wang, Shuo, editor, and Young, Alistair, editor

Published
2022
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29. Zika virus infection in pregnant travellers and impact on childhood neurodevelopment in the first two years of life: A prospective observational study

Author

Marbán-Castro, Elena, Goncé, Anna, Fumadó, Victoria, Martínez, Miguel J., López, Marta, García-Otero, Laura, Salazar, Laura, Esteve, Carolina, Salvia, Dolors, Fortuny, Claudia, Eixarch, Elisenda, Fuente-Moreno, Marina, Pinazo, María Jesús, Oliveira, Inés, Rodríguez-Valero, Natalia, Requena-Méndez, Ana, Camprubí, Daniel, Saco, Adela, Castillo, Paola, Vázquez, Ana, de Ory, Fernando, Navero-Castillejos, Jessica, Casellas, Aina, González, Raquel, Muñoz, José, Gascón, Joaquim, Ordi, Jaume, Menéndez, Clara, and Bardají, Azucena

Published
2021
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31. Haemodynamic Changes in the Fetal Circulation Post-connection to an Artificial Placenta: A Computational Modelling Study

Author

Villanueva, Maria Inmaculada, primary, López, Marc, additional, Sánchez, Sergio, additional, Garcia-Cañadilla, Patricia, additional, Randanne, Paula C., additional, Hawkins, Ameth, additional, Eixarch, Elisenda, additional, Bonet-Carne, Elisenda, additional, Gratacós, Eduard, additional, Crispi, Fàtima, additional, Bijnens, Bart, additional, and Bernardino, Gabriel, additional

Published
2022
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34. Cohort Profile: Barcelona Life Study Cohort (BiSC)

Author

Dadvand, Payam, Gascon, Mireia, Bustamante, Mariona, Rivas, Ioar, Foraster, Maria, Basagaña, Xavier, Cosín, Marta, Eixarch, Elisenda, Ferrer, Muriel, Gratacós, Eduard, Gómez Herrera, Laura, Jimenez-Arenas, Pol, Júlvez, Jordi, Morillas, Àlex, Nieuwenhuijsen, Mark J., Persavento, Cecília, Pujol, Jesús, Querol, Xavier, Sánchez García, Olga, Vrijheid, Martine, Llurba, Elisa, Gómez-Roig, María Dolores, Sunyer, Jordi, Dadvand, Payam, Gascon, Mireia, Bustamante, Mariona, Rivas, Ioar, Foraster, Maria, Basagaña, Xavier, Cosín, Marta, Eixarch, Elisenda, Ferrer, Muriel, Gratacós, Eduard, Gómez Herrera, Laura, Jimenez-Arenas, Pol, Júlvez, Jordi, Morillas, Àlex, Nieuwenhuijsen, Mark J., Persavento, Cecília, Pujol, Jesús, Querol, Xavier, Sánchez García, Olga, Vrijheid, Martine, Llurba, Elisa, Gómez-Roig, María Dolores, and Sunyer, Jordi

Abstract

This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (785994—AirNB project) and the Health Effects Institute (HEI), an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No. R-82811201) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. A full list of the funding sources that supported specific parts of the project can be found at https://projectebisc.org/en/funding-sources/. ISGlobal acknowledges support from the grant CEX2018-000806-S funded by MCIN/AEI/10.13039/501100011033 and support from the Generalitat de Catalunya through the CERCA Program. Mireia Gascon holds a Miguel Servet fellowship (Grant CP19/00183) funded by Acción Estratégica de Salud—Instituto de Salud Carlos III, co-funded by European Social Fund ‘Investing in your future’. Ioar Rivas received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (Grant Agreement No. 886121) and Ramón y Cajal fellowship (RYC2021-032781-I), funded by the MCIN/AEI/10.13039/501100011033 and the European Union «NextGenerationEU»/PRTR. Elisenda Eixarch has received funding from ‘Convocatòria Intensificació Interna per als professionals de l’Hospital Clínic de Barcelona 2023’, granted by Hospital Clínic de Barcelona.

Published
2024

36. Classification

Author

Helfer, Talita Micheletti, Eixarch, Elisenda, Figueras, Francesc, Gratacós, Eduard, Nardozza, Luciano Marcondes Machado, editor, Araujo Júnior, Edward, editor, Rizzo, Giuseppe, editor, and Deter, Russell Lee, editor

Published
2019
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42. Deep Cascaded Registration and Weakly-Supervised Segmentation of Fetal Brain MRI

Author

Comte, Valentin, primary, Alenya, Mireia, additional, Urru, Andrea, additional, Recober, Judith, additional, Nakaki, Ayako, additional, Crovetto, Francesca, additional, Camara, Oscar, additional, Gratacós, Eduard, additional, Eixarch, Elisenda, additional, Crispi, Fátima, additional, Piella, Gemma, additional, Ceresa, Mario, additional, and González Ballester, Miguel Ángel, additional

Published
2024
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45. Diurnal cortisol throughout pregnancy and its association with maternal depressive symptoms and birth outcomes

Author

Castro-Quintas, Águeda, primary, Eixarch, Elisenda, additional, Martin-Gonzalez, Nerea San, additional, Daura-Corral, Maria, additional, Marques-Feixa, Laia, additional, Palma-Gudiel, Helena, additional, Rocavert-Barranco, Mireia, additional, Miguel-Valero, Alba, additional, Monteserín-García, Jose Luis, additional, de la Fuente-Tomás, Lorena, additional, Crispi, Fátima, additional, Arias, Barbara, additional, García-Portilla, María Paz, additional, and Fañanás, Lourdes, additional

Published
2023
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46. Preoperative Planning and Simulation Framework for Twin-to-Twin Transfusion Syndrome Fetal Surgery

Author

Torrents-Barrena, Jordina, López-Velazco, Rocío, Masoller, Narcís, Valenzuela-Alcaraz, Brenda, Gratacós, Eduard, Eixarch, Elisenda, Ceresa, Mario, González Ballester, Miguel Ángel, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Stoyanov, Danail, editor, Taylor, Zeike, editor, Sarikaya, Duygu, editor, McLeod, Jonathan, editor, González Ballester, Miguel Angel, editor, Codella, Noel C.F., editor, Martel, Anne, editor, Maier-Hein, Lena, editor, Malpani, Anand, editor, Zenati, Marco A., editor, De Ribaupierre, Sandrine, editor, Xiongbiao, Luo, editor, Collins, Toby, editor, Reichl, Tobias, editor, Drechsler, Klaus, editor, Erdt, Marius, editor, Linguraru, Marius George, editor, Oyarzun Laura, Cristina, editor, Shekhar, Raj, editor, Wesarg, Stefan, editor, Celebi, M. Emre, editor, Dana, Kristin, editor, and Halpern, Allan, editor

Published
2018
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47. Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding

Author

Benkarim, Oualid M., Sanroma, Gerard, Piella, Gemma, Rekik, Islem, Hahner, Nadine, Eixarch, Elisenda, González Ballester, Miguel Angel, Shen, Dinggang, Li, Gang, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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