Publications

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain.Kebiri H., Canales-Rodríguez E.J., Lajous H., de Dumast P., Girard G., Alemán-Gómez Y., Koob M., Jakab A., Bach Cuadra M., 2022. Frontiers in neurology, 13 p. 827816. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_BD4D1BDA9F16]

Assessment of fetal corpus callosum biometry by 3D super-resolution reconstructed T2-weighted magnetic resonance imaging.Lamon S., de Dumast P., Sanchez T., Dunet V., Pomar L., Vial Y., Koob M., Bach Cuadra M., 2024. Frontiers in neurology, 15 p. 1358741. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_569D839EC78C]

Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation.Vidal J.P., Danet L., Péran P., Pariente J., Bach Cuadra M., Zahr N.M., Barbeau E.J., Saranathan M. Brain structure & function. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_9604D6F4D5B0]

Transfer learning with weak labels from radiology reports: application to glioma change detectionDi Noto Tommaso, Bach Cuadra Meritxell, Atat Chirine, Gamito Teiga Eduardo, Hegi Monika, Hottinger Andreas, Hagmann Patric, Richiardi Jonas, 2022..[URN][DOI][serval:BIB_9CC85EE111CA]

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.La Rosa F., Beck E.S., Maranzano J., Todea R.A., van Gelderen P., de Zwart J.A., Luciano N.J., Duyn J.H., Thiran J.P., Granziera C. et al., 2022/08. NMR in biomedicine, 35 (8) pp. e4730. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_AA2BF0088C38]

A New Advanced MRI Biomarker for Remyelinated Lesions in Multiple Sclerosis.Rahmanzadeh R., Galbusera R., Lu P.J., Bahn E., Weigel M., Barakovic M., Franz J., Nguyen T.D., Spincemaille P., Schiavi S. et al., 2022/09. Annals of neurology, 92 (3) pp. 486-502. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_A7E8C017F374]

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review.Spagnolo F., Depeursinge A., Schädelin S., Akbulut A., Müller H., Barakovic M., Melie-Garcia L., Bach Cuadra M., Granziera C., 2023. NeuroImage. Clinical, 39 p. 103491. Peer-reviewed.[URN][DOI][Pmid][serval:BIB_72B208896093]

A multi-scale probabilistic atlas of the human connectome.Alemán-Gómez Y., Griffa A., Houde J.C., Najdenovska E., Magon S., Cuadra M.B., Descoteaux M., Hagmann P., 2022/08/23. Scientific data, 9 (1) p. 516. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_493CEE0FC93E]

Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome MappingTourbier Sebastien, Rue-Queralt Joan, Glomb Katharina, Aleman-Gomez Yasser, Mullier Emeline, Griffa Alessandra, Schöttner Mikkel, Wirsich Jonathan, Tuncel M. Anıl, Jancovic Jakub et al., 2022/06/27. Journal of Open Source Software, 7 (74) p. 4248. Peer-reviewed.[DOI][serval:BIB_F8FCF7203C80]

FetMRQC: Automated Quality Control for Fetal Brain MRISanchez Thomas, Esteban Oscar, Gomez Yvan, Eixarch Elisenda, Cuadra Meritxell Bach, 2023. pp. 3-16 dans Lecture Notes in Computer Science, Springer Nature Switzerland.[DOI][serval:BIB_C79FCE1D3BDA]

Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstructionde Dumast Priscille, Sanchez Thomas, Lajous Hélène, Bach Cuadra Meritxell, 2023. pp. 336-346 dans Lecture Notes in Computer Science, Springer Nature Switzerland.[DOI][serval:BIB_B08F2822DE01]

Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability.de Matos K., Cury C., Chougar L., Strike L.T., Rolland T., Riche M., Hemforth L., Martin A., Banaschewski T., Bokde ALW et al., 2023/07. Brain structure & function, 228 (6) pp. 1459-1478. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_A75D868C7257]

BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development.Sainz Martinez C., Bach Cuadra M., Jorge J., 2023/06. NeuroImage, 273 p. 120074. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_87E26260F80E]

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues.La Rosa F., Wynen M., Al-Louzi O., Beck E.S., Huelnhagen T., Maggi P., Thiran J.P., Kober T., Shinohara R.T., Sati P. et al., 2022. NeuroImage. Clinical, 36 p. 103205. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_4506B8D09769]

BMAT: An open-source BIDS managing and analysis tool.Vanden Bulcke C., Wynen M., Detobel J., La Rosa F., Absinta M., Dricot L., Macq B., Bach Cuadra M., Maggi P., 2022. NeuroImage. Clinical, 36 p. 103252. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_E3F355A84351]

An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak LabelsDi Noto Tommaso, Marie Guillaume, Tourbier Sébastien, Alemán-Gómez Yasser, Saliou Guillaume, Bach Cuadra Meritxell, Hagmann Patric, Richiardi Jonas, 2020. pp. 56-66 dans Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, Springer International Publishing.[URN][DOI][serval:BIB_C85E63E2380A]

Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal studyKebiri Hamza, Gholipour Ali, Vasung Lana, Lin Rizhong, Krsnik Željka, Karimi Davood, Bach Cuadra Meritxell.[DOI][Pmid][serval:BIB_4A067369CFE5]

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis.Barquero G., La Rosa F., Kebiri H., Lu P.J., Rahmanzadeh R., Weigel M., Fartaria M.J., Kober T., Théaudin M., Du Pasquier R. et al., 2020. NeuroImage. Clinical, 28 p. 102412. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_765C195C3334]

An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset.Payette K., de Dumast P., Kebiri H., Ezhov I., Paetzold J.C., Shit S., Iqbal A., Khan R., Kottke R., Grehten P. et al., 2021/07/06. Scientific data, 8 (1) p. 167. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_6EB3F06B58CA]

Deep learning methods for diffusion MRI in early development of the human brain: resolution enhancement and model estimationKebiri Hamza, 2023/08/02., Université de Lausanne, Faculté de biologie et médecine, Bach Cuadra Meritxell (dir.).[URN][serval:BIB_B248E402983E]

A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects.Rahmanzadeh R., Weigel M., Lu P.J., Melie-Garcia L., Nguyen T.D., Cagol A., La Rosa F., Barakovic M., Lutti A., Wang Y. et al., 2022. NeuroImage. Clinical, 36 p. 103177. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_460AAADA27D4]

A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation mapsNguyen Huu-Giao, Pica Alessia, Hrbacek Jan, Weber Damien C., La Rosa Francesco, Schalenbourg Ann, Sznitman Raphael, Bach Cuadra Meritxell, 2019/07/08., Medical Imaging with Deep Learning London, 8 ‑ 10 July 2019 pp. 370–379 dans Proceedings of Machine Learning Research, Proceedings of Machine Learning […]

Translating fetal brain magnetic resonance image super-resolution reconstruction into the clinical environmentde Dumast Priscille, Deman Pierre, Khawam Marie, Yu Thomas, Tourbier Sébastien, Lajous Hélène, Hagmann Patric, Maeder Philippe, Thiran Jean-Philippe, Meuli Reto et al., 2020/02/26..[URN][serval:BIB_0B1137535903]

Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T.Jorge J., Gretsch F., Najdenovska E., Tuleasca C., Levivier M., Maeder P., Gallichan D., Marques J.P., Bach Cuadra M., 2020/09. Magnetic resonance in medicine, 84 (3) pp. 1218-1234. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_0D315EB8ED5C]

Development and validation of robust MR image reconstruction and segmentation techniques for the quantitative analysis of the fetal brainGuerrier de Dumast Priscille, 2023., Université de Lausanne, Faculté de biologie et médecine, Bach Cuadra Meritxell (dir.).[URN][serval:BIB_F3F67E24C1DE]

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.La Rosa F., Abdulkadir A., Fartaria M.J., Rahmanzadeh R., Lu P.J., Galbusera R., Barakovic M., Thiran J.P., Granziera C., Cuadra M.B., 2020. NeuroImage. Clinical, 27 p. 102335. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_5FF43232BB1C]

Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge.Di Noto T., Marie G., Tourbier S., Alemán-Gómez Y., Esteban O., Saliou G., Cuadra M.B., Hagmann P., Richiardi J., 2023/01. Neuroinformatics, 21 (1) pp. 21-34. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_B6A1DE874D8B]

Multimodal Magnetic Resonance Imaging Depicts Widespread and Subregion Specific Anomalies in the Thalamus of Early-Psychosis and Chronic Schizophrenia Patients.Alemán-Gómez Y., Baumgartner T., Klauser P., Cleusix M., Jenni R., Hagmann P., Conus P., Do K.Q., Bach Cuadra M., Baumann P.S. et al., 2023/01/03. Schizophrenia bulletin, 49 (1) pp. 196-207. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_18D066077BD6]

Editorial: Computational Neuroimage Analysis Tools for Brain (Diseases) Biomarkers.Sima D.M., Bach Cuadra M., Dyrby T.B., Van Leemput K., 2022. Frontiers in neuroscience, 16 p. 841807. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_DB40F93599BE]

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).Lajous H., Roy C.W., Hilbert T., de Dumast P., Tourbier S., Alemán-Gómez Y., Yerly J., Yu T., Kebiri H., Payette K. et al., 2022/05/23. Scientific reports, 12 (1) p. 8682. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_9F910B893D7D]

Chronic White Matter Inflammation and Serum Neurofilament Levels in Multiple Sclerosis.Maggi P., Kuhle J., Schädelin S., van der Meer F., Weigel M., Galbusera R., Mathias A., Lu P.J., Rahmanzadeh R., Benkert P. et al., 2021/08/10. Neurology, 97 (6) pp. e543-e553. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_6A4C8678004D]

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology.Lu P.J., Yoo Y., Rahmanzadeh R., Galbusera R., Weigel M., Ceccaldi P., Nguyen T.D., Spincemaille P., Wang Y., Daducci A. et al., 2021. NeuroImage. Clinical, 29 p. 102522. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_39876666592D]

T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance AcquisitionsLajous Hélène, Hilbert Tom, Roy Christopher W., Tourbier Sébastien, de Dumast Priscille, Yu Thomas, Thiran Jean-Philippe, Ledoux Jean-Baptiste, Piccini Davide, Hagmann Patric et al., 2020. pp. 114-124 dans Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Springer International […]

Automated fetal brain segmentation of 2D magnetic resonance images: transfer learning and 3D topology correctionKebiri Hamza, de Dumast Priscille, Yu Thomas, Lajous Hélène, Thiran Jean-Philippe, Meuli Reto, Koob Meriam, Bach Cuadra Meritxell, 2020/02/26..[URN][serval:BIB_7C1BE8E72718]

Partial‐volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophreniaAlemán-Gómez Yasser, Najdenovska Elena, Roine Timo, Fartaria Mário João, Canales-Rodríguez Erick J., Rovó Zita, Hagmann Patric, Conus Philippe, Do Kim Q., Klauser Paul et al., 2020/10. Human Brain Mapping, 41 (14) pp. 4041-4061. […]

Model-informed machine learning for multi-component T₂ relaxometry.Yu T., Canales-Rodríguez E.J., Pizzolato M., Piredda G.F., Hilbert T., Fischi-Gomez E., Weigel M., Barakovic M., Bach Cuadra M., Granziera C. et al., 2021/04. Medical image analysis, 69 p. 101940. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_934FD7FA4F63]

Diagnostic surveillance of high-grade gliomas: towards automated change detection using radiology report classificationDi Noto Tommaso, Atat Chirine, Teiga Eduardo Gamito, Hegi Monika, Hottinger Andreas, Bach Cuadra Meritxell, Hagmann Patric, Richiardi Jonas, 2022/01/01. pp. 423-436 dans Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021., Communications in Computer and […]

Quantitative Evaluation of Enhanced Multi-plane Clinical Fetal Diffusion MRI with a Crossing-Fiber PhantomKebiri Hamza, Lajous Hélène, Alemán-Gómez Yasser, Girard Gabriel, Rodríguez Erick Canales, Tourbier Sébastien, Pizzolato Marco, Ledoux Jean-Baptiste, Fornari Eleonora, Jakab András et al., 2021. pp. 12-22 dans Computational Diffusion MRI, Springer International […]

Ophthalmic Magnetic Resonance Imaging: Where Are We (Heading To)?Niendorf T., Beenakker J.M., Langner S., Erb-Eigner K., Bach Cuadra M., Beller E., Millward J.M., Niendorf T.M., Stachs O., 2021/09. Current eye research, 46 (9) pp. 1251-1270. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_0D766C431EA3]

Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma.Strijbis VIJ, de Bloeme C.M., Jansen R.W., Kebiri H., Nguyen H.G., de Jong M.C., Moll A.C., Bach-Cuadra M., de Graaf P., Steenwijk M.D., 2021/07/16. Scientific reports, 11 (1) p. 14590. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_61A3D7FC211B]

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.Lu P.J., Barakovic M., Weigel M., Rahmanzadeh R., Galbusera R., Schiavi S., Daducci A., La Rosa F., Bach Cuadra M., Sandkühler R. et al., 2021. Frontiers in neuroscience, 15 p. 647535. […]

Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging.Rahmanzadeh R., Lu P.J., Barakovic M., Weigel M., Maggi P., Nguyen T.D., Schiavi S., Daducci A., La Rosa F., Schaedelin S. et al., 2021/07/28. Brain, 144 (6) pp. 1684-1696. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_1C5A25DB7291]

Fetal Brain Biometric Measurements on 3D Super-Resolution Reconstructed T2-Weighted MRI: An Intra- and Inter-observer Agreement Study.Khawam M., de Dumast P., Deman P., Kebiri H., Yu T., Tourbier S., Lajous H., Hagmann P., Maeder P., Thiran J.P. et al., 2021. Frontiers in pediatrics, 9 p. 639746. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_0C9548890931]

Fetal brain biometrics: comparison of 2D T2-weighted and 3D volumetric super-resolution magnetic resonance imagingKhawam Marie, de Dumast Priscille, Deman Pierre, Kebiri Hamza, Yu Thomas, Tourbier Sébastien, Lajous Hélène, Hagmann Patric, Maeder Philippe, Thiran Jean-Philippe et al., 2020/02/26..[URN][serval:BIB_36798A98FA8E]

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients.La Rosa F., Yu T., Barquero G., Thiran J.P., Granziera C., Bach Cuadra M., 2021/05. Computers in biology and medicine, 132 p. 104297. Peer-reviewed.[DOI][WoS][Pmid][serval:BIB_F08CC4CD5E7D]

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics.Bach Cuadra M., Favre J., Omoumi P., 2020/02. Seminars in musculoskeletal radiology, 24 (1) pp. 50-64. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_93C1E02D6980]

Evolution of Cortical and White Matter Lesion Load in Early-Stage Multiple Sclerosis: Correlation With Neuroaxonal Damage and Clinical Changes.Todea R.A., Lu P.J., Fartaria M.J., Bonnier G., Du Pasquier R., Krueger G., Bach Cuadra M., Psychogios M.N., Kappos L., Kuhle J. et al., 2020. Frontiers in neurology, 11 p. 973. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_368B9441C688]

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.Maggi P., Fartaria M.J., Jorge J., La Rosa F., Absinta M., Sati P., Meuli R., Du Pasquier R., Reich D.S., Bach Cuadra M. et al., 2020/05. NMR in biomedicine, 33 (5) pp. e4283. Peer-reviewed.[URN][DOI][WoS][Pmid][serval:BIB_16E178964567]

Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRILa Rosa Francesco, Beck Erin S., Abdulkadir Ahmed, Thiran Jean-Philippe, Reich Daniel S., Sati Pascal, Bach Cuadra Meritxell, 2020/10/04., 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV dans Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Peer-reviewed, Prof. Anne L. […]

Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance AcquisitionsLajous Hélène, Ledoux Jean-Baptiste, Hilbert Tom, van Heeswijk Ruud B., Bach Cuadra Meritxell, 2020/10/12..[DOI][serval:BIB_36F6C59AC463]