Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n = 75 sites, 1,631 participants) dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model's predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently introduced normative database of healthy participants containing commonly used measures of spinal cord morphometry. Results showed that (i) our model performs well compared with its previous versions and existing pathology-specific models on the lumbar spinal cord, images with severe compression, and in the presence of intramedullary lesions and/or atrophy achieving an average Dice score of 0.95 ± 0.03; (ii) the automatic workflow for monitoring morphometric drift provides a quick feedback loop for developing future segmentation models; and (iii) the scaling factor required to update the database of morphometric measures is nearly constant among slices across the given vertebral levels, showing minimum drift between the current and previous versions of the model monitored by the framework. The code and model are open source and accessible via Spinal Cord Toolbox v7.0.

Aix Marseille Univ CNRS CRMBM Marseille France

APHM CHU Timone CEMEREM Marseille France

Athinoula A Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA; Harvard Medical School Boston MA United States

Barlo MS Centre Division of Neurology Department of Medicine St Michael's Hospital Toronto Canada

Canada CIFAR AI Chair Toronto ON Canada

Centre de Recherche du CHU Sainte Justine Université de Montréal Montréal QC Canada

Department of Anesthesiology Perioperative and Pain Medicine Stanford University School of Medicine Palo Alto CA United States

Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden

Department of Medicine Division of Neurology University of British Columbia BC Canada

Department of Neuro Urology Balgrist University Hospital University of Zurich Zurich Switzerland

Department of Neurology Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia

Department of Neurology Pitie Salpetriere Hospital Paris France

Department of Neurology University Hospital Brno Brno Czechia

Department of Neuroradiology Karolinska University Hospital Stockholm Sweden

Department of Neuroradiology Neurocenter of Southern Switzerland Lugano Switzerland

Department of Neuroradiology Rennes University Hospital Rennes France

Department of Neuroscience Imaging and Clinical Sciences Università G d'Annunzio Chieti Italy

Department of Neuroscience Université de Montréal Montréal QC Canada

Department of Neurosurgery Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia

Department of Neurosurgery University of California Davis Davis CA United States

Department of Physical Medicine and Rehabilitation University of Colorado School of Medicine Aurora CO United States

Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland

Division of Neurology Department of Medicine and the Djavad Mowafaghian Centre for Brain Health University of British Columbia Vancouver BC Canada

Division of Neurosurgery and Spine Program Department of Surgery Temerty Faculty of Medicine University of Toronto Toronto ON Canada

Division of Neurosurgery Krembil Neuroscience Centre University Health Network Toronto ON Canada

Division of Pain Medicine Department of Anesthesiology Perioperative and Pain Medicine Stanford University School of Medicine Palo Alto CA United States

EMPENN Research Team IRISA CNRS‑INSERM‑INRIA Rennes Université Rennes France

Faculty of Medicine Masaryk University Brno Czechia

Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada

Institute of Medical Science University of Toronto Toronto ON Canada

Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany

Max Planck Research Group MR Physics Max Planck Institute for Human Development Berlin Germany

Max Planck Research Group Pain Perception Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany

Mila Quebec AI Institute Montreal QC Canada

Multimodal and Functional Imaging Laboratory Central European Institute of Technology Brno Czechia

Neuro 10 Institute Ecole Polytechnique Fédérale de Lausanne Geneva Switzerland

Neurology Department Rennes University Hospital Rennes France

NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada

NMR Research Unit Queen Square Multiple Sclerosis Centre UCL Queen Square Institute of Neurology University College London London United Kingdom

Physikalisch Technische Bundesanstalt Braunschweig and Berlin Germany

Praxis Spinal Cord Institute Vancouver BC Canada

qMRI Core Facility National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MD United States

School of Biomedical Engineering Department of Radiology The University of British Columbia Vancouver BC Canada

Spinal Cord Injury Center Balgrist University Hospital University of Zurich Zurich Switzerland

Translational Imaging in Neurology Department of Biomedical Engineering Faculty of Medicine Basel Switzerland

Translational Neuroradiology Section National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MD United States

Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN United States

Wellcome Trust Centre for Neuroimaging Queen Square Institute of Neurology University College London London United Kingdom

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ArXiv. 2025 May 2:arXiv:2505.01364v1. PubMed

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Agirre, E., Jonsson, A., & Larcher, A. (2021). Framing lifelong learning as autonomous deployment: Tune once live forever. In Lecture Notes in Electrical Engineering (pp. 331–336). Springer. 10.1007/978-981-15-9323-9_29 DOI

Alla, S., & Adari, S. K. (2020). Beginning MLOps with MLFlow: Deploy models in AWS SageMaker, Google cloud, and Microsoft azure (1st ed.). Apress. 10.1007/978-1-4842-6549-9 DOI

Bautin, P., & Cohen-Adad, J. (2021). Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants. NeuroImage. Clinical, 32, 102849. 10.1016/j.nicl.2021.102849 PubMed DOI PMC

Bédard, S., & Cohen-Adad, J. (2022). Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Frontiers in Neuroimaging, 1, 1031253. 10.3389/fnimg.2022.1031253 PubMed DOI PMC

Bédard, S., Karthik, E. N., Tsagkas, C., Pravatà, E., Granziera, C., Smith, A., Weber, K. A., Ii, & Cohen-Adad, J. (2025). Towards contrast-agnostic soft segmentation of the spinal cord. Medical Image Analysis, 101, 103473. 10.1016/j.media.2025.103473 PubMed DOI

Bédard, S., Valošek, J., Seif, M., Curt, A., Schading, S., Pfender, N., Freund, P., Hupp, M., & Cohen-Adad, J. (2024). Normalizing spinal cord compression morphometric measures: Application in degenerative cervical myelopathy. medRxiv. 10.1101/2024.03.13.24304177 PubMed DOI

Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv [cs.CV]. http://arxiv.org/abs/1706.05587

Chen, M., Carass, A., Oh, J., Nair, G., Pham, D. L., Reich, D. S., & Prince, J. L. (2013). Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view. NeuroImage, 83, 1051–1062. 10.1016/j.neuroimage.2013.07.060 PubMed DOI PMC

Cohen-Adad, J., Alonso-Ortiz, E., Abramovic, M., Arneitz, C., Atcheson, N., Barlow, L., Barry, R. L., Barth, M., Battiston, M., Büchel, C., Budde, M., Callot, V., Combes, A. J. E., De Leener, B., Descoteaux, M., de Sousa, P. L., Dostál, M., Doyon, J., Dvorak, A., … Xu, J. (2021a). Generic acquisition protocol for quantitative MRI of the spinal cord. Nature Protocols, 16(10), 4611–4632. 10.1038/s41596-021-00588-0 PubMed DOI PMC

Cohen-Adad, J., Alonso-Ortiz, E., Abramovic, M., Arneitz, C., Atcheson, N., Barlow, L., Barry, R. L., Barth, M., Battiston, M., Büchel, C., Budde, M., Callot, V., Combes, A. J. E., De Leener, B., Descoteaux, M., de Sousa, P. L., Dostál, M., Doyon, J., Dvorak, A., … Xu, J. (2021b). Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Scientific Data, 8(1), 219. 10.1038/s41597-021-00941-8 PubMed DOI PMC

Commowick, O., Istace, A., Kain, M., Laurent, B., Leray, F., Simon, M., Pop, S. C., Girard, P., Améli, R., Ferré, J.-C., Kerbrat, A., Tourdias, T., Cervenansky, F., Glatard, T., Beaumont, J., Doyle, S., Forbes, F., Knight, J., Khademi, A., … Barillot, C. (2018). Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure. Scientific Reports, 8(1), 13650. 10.1038/s41598-018-31911-7 PubMed DOI PMC

Connor, J. R., Thornton, W. A., Weber, K. A., Pfyffer, D., Freund, P., Tefertiller, C., & Smith, A. C. (2025). Reliability of SCIseg automated measurement of midsagittal tissue bridges in spinal cord injuries using an external dataset. Topics in Spinal Cord Injury Rehabilitation, 31(2), 39–49. 10.46292/sci25-00015 PubMed DOI PMC

De Leener, B., Kadoury, S., & Cohen-Adad, J. (2014). Robust, accurate and fast automatic segmentation of the spinal cord. NeuroImage, 98, 528–536. 10.1016/j.neuroimage.2014.04.051 PubMed DOI

De Leener, B., Lévy, S., Dupont, S. M., Fonov, V. S., Stikov, N., Louis Collins, D., Callot, V., & Cohen-Adad, J. (2017). SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. NeuroImage, 145(Pt A), 24–43. 10.1016/j.neuroimage.2016.10.009 PubMed DOI

Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., & Heng, P.-A. (2017). 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, 41, 40–54. 10.1016/j.media.2017.05.001 PubMed DOI

González, C., Fuchs, M., Santos, D. P. dos, Matthies, P., Trenz, M., Grüning, M., Chaudhari, A., Larson, D. B., Othman, A., Kim, M., Nensa, F., & Mukhopadhyay, A. (2024). Regulating radiology AI medical devices that evolve in their lifecycle. arXiv [cs.CY]. http://arxiv.org/abs/2412.20498

Gros, C., De Leener, B., Badji, A., Maranzano, J., Eden, D., Dupont, S. M., Talbott, J., Zhuoquiong, R., Liu, Y., Granberg, T., Ouellette, R., Tachibana, Y., Hori, M., Kamiya, K., Chougar, L., Stawiarz, L., Hillert, J., Bannier, E., Kerbrat, A., … Cohen-Adad, J. (2019). Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. NeuroImage, 184, 901–915. 10.1016/j.neuroimage.2018.09.081 PubMed DOI PMC

Horáková, M., Horák, T., Valošek, J., Rohan, T., Korit’áková, E., Dostál, M., Kočica, J., Skutil, T., Keřkovský, M., Kadaňka, Z., Jr, Bednařík, P., Svátková, A., Hluštík, P., & Bednařík, J. (2022). Semi-automated detection of cervical spinal cord compression with the Spinal Cord Toolbox. Quantitative Imaging in Medicine and Surgery, 12(4), 2261–2279. 10.21037/qims-21-782 PubMed DOI PMC

Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. 10.1038/s41592-020-01008-z PubMed DOI

Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., & Jaeger, P. F. (2024). NnU-Net revisited: A call for rigorous validation in 3D medical image segmentation. arXiv [cs.CV]. 10.48550/ARXIV.2404.09556 DOI

Joo, B., Park, H. J., Park, M., Suh, S. H., & Ahn, S. J. (2025). Establishing normative values for entire spinal cord morphometrics in East Asian young adults. Korean Journal of Radiology: Official Journal of the Korean Radiological Society, 26(2), 146–155. 10.3348/kjr.2024.0907 PubMed DOI PMC

Jwa, A. S., Norgaard, M., & Poldrack, R. A. (2025). Can I have your data? Recommendations and practical tips for sharing neuroimaging data upon a direct personal request. Imaging Neuroscience, 3, imag_a_00508. 10.1162/imag_a_00508 PubMed DOI PMC

Kandpal, N., Lester, B., Muqeeth, M., Mascarenhas, A., Evans, M., Baskaran, V., Huang, T., Liu, H., & Raffel, C. (2023). Git-Theta: A Git extension for collaborative development of machine learning models. In arXiv [cs.LG]. http://arxiv.org/abs/2306.04529

Karthik, E. N., Kerbrat, A., Labauge, P., Granberg, T., Talbott, J., Reich, D. S., Filippi, M., Bakshi, R., Callot, V., Chandar, S., & Cohen-Adad, J. (2022). Segmentation of Multiple Sclerosis lesions across hospitals: Learn continually or train from scratch? arXiv [cs.CV]. http://arxiv.org/abs/2210.15091

Karthik, E. N., Valošek, J., Farner, L., Pfyffer, D., Schading-Sassenhausen, S., Lebret, A., David, G., Smith, A. C., Weber, K. A., II, Seif, M., RHSCIR Network Imaging Group, Freund, P., & Cohen-Adad, J. (2024). SCIsegV2: A universal tool for segmentation of intramedullary lesions in spinal cord injury. arXiv [cs.CV]. http://arxiv.org/abs/2407.17265

Kato, F., Yukawa, Y., Suda, K., Yamagata, M., & Ueta, T. (2012). Normal morphology, age-related changes and abnormal findings of the cervical spine. Part II: Magnetic resonance imaging of over 1,200 asymptomatic subjects. European Spine Journal: Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 21, 1499–1507. https://link.springer.com/article/10.1007/s00586-012-2176-4 PubMed PMC

Labounek, R., Bondy, M. T., Paulson, A. L., Bédard, S., Abramovic, M., Alonso-Ortiz, E., Atcheson, N. T., Barlow, L. R., Barry, R. L., Barth, M., Battiston, M., Büchel, C., Budde, M. D., Callot, V., Combes, A., Leener, B. D., Descoteaux, M., Loureiro de Sousa, P., Dostál, M., … Nestrašil, I. (2024). Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study. bioRxiv. 10.1101/2024.04.29.591421 PubMed DOI PMC

Lemay, A., Gros, C., Karthik, E. N., & Cohen-Adad, J. (2022). Label fusion and training methods for reliable representation of inter-rater uncertainty. arXiv [eess.IV]. http://arxiv.org/abs/2202.07550

Liu, B., & Mazumder, S. (2021). Lifelong and continual learning dialogue systems: Learning during conversation. Proceedings of the . . . AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 35(17), 15058–15063. 10.1609/aaai.v35i17.17768 DOI

Losseff, N. A., Webb, S. L., O’Riordan, J. I., Page, R., Wang, L., Barker, G. J., Tofts, P. S., McDonald, W. I., Miller, D. H., & Thompson, A. J. (1996). Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression. Brain: A Journal of Neurology, 119(Pt 3), 701–708. 10.1093/brain/119.3.701 PubMed DOI

Lukas, C., Sombekke, M. H., Bellenberg, B., Hahn, H. K., Popescu, V., Bendfeldt, K., Radue, E. W., Gass, A., Borgwardt, S. J., Kappos, L., Naegelin, Y., Knol, D. L., Polman, C. H., Geurts, J. J. G., Barkhof, F., & Vrenken, H. (2013). Relevance of spinal cord abnormalities to clinical disability in multiple sclerosis: MR imaging findings in a large cohort of patients. Radiology, 269(2), 542–552. 10.1148/radiol.13122566 PubMed DOI

Martin, A. R., De Leener, B., Cohen-Adad, J., Kalsi-Ryan, S., Cadotte, D. W., Wilson, J. R., Tetreault, L., Nouri, A., Crawley, A., Mikulis, D. J., Ginsberg, H., Massicotte, E. M., & Fehlings, M. G. (2018). Monitoring for myelopathic progression with multiparametric quantitative MRI. PLoS One, 13(4), e0195733. 10.1371/journal.pone.0195733 PubMed DOI PMC

Masse-Gignac, N., Flórez-Jiménez, S., Mac-Thiong, J.-M., & Duong, L. (2023). Attention-gated U-Net networks for simultaneous axial/sagittal planes segmentation of injured spinal cords. Journal of Applied Clinical Medical Physics, 24(10), e14123. 10.1002/acm2.14123 PubMed DOI PMC

Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. arXiv [cs.CV]. http://arxiv.org/abs/1606.04797

Molinier, N., Bédard, S., Boudreau, M., Cohen-Adad, J., Callot, V., Alonso-Ortiz, E., Pageot, C., & Laines-Medina, N. (2024). “whole-spine” [Dataset]. OpenNeuro. 10.18112/openneuro.ds005616.v1.0.1 DOI

Naga Karthik, E., McGinnis, J., Wurm, R., Ruehling, S., Graf, R., Valosek, J., Benveniste, P.-L., Lauerer, M., Talbott, J., Bakshi, R., Tauhid, S., Shepherd, T., Berthele, A., Zimmer, C., Hemmer, B., Rueckert, D., Wiestler, B., Kirschke, J. S., Cohen-Adad, J., & Mühlau, M. (2025). Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans. medRxiv. 10.1101/2025.01.22.25320959 PubMed DOI PMC

Naga Karthik, E., Valošek, J., Smith, A. C., Pfyffer, D., Schading-Sassenhausen, S., Farner, L., Weber, K. A., 2nd, Freund, P., & Cohen-Adad, J. (2025). SCIseg: Automatic segmentation of intramedullary lesions in spinal cord injury on T2-weighted MRI scans. Radiology. Artificial Intelligence, 7(1), e240005. 10.1148/ryai.240005 PubMed DOI PMC

Nozawa, K., Maki, S., Furuya, T., Okimatsu, S., Inoue, T., Yunde, A., Miura, M., Shiratani, Y., Shiga, Y., Inage, K., Eguchi, Y., Ohtori, S., & Orita, S. (2023). Magnetic resonance image segmentation of the compressed spinal cord in patients with degenerative cervical myelopathy using convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery, 18(1), 45–54. 10.1007/s11548-022-02783-0 PubMed DOI

Papinutto, N., Asteggiano, C., Bischof, A., Gundel, T. J., Caverzasi, E., Stern, W. A., Bastianello, S., Hauser, S. L., & Henry, R. G. (2020). Intersubject variability and normalization strategies for spinal cord total cross-sectional and gray matter areas. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging, 30(1), 110–118. 10.1111/jon.12666 PubMed DOI PMC

Prapas, I., Derakhshan, B., Mahdiraji, A. R., & Markl, V. (2021). Continuous training and deployment of deep learning models. Datenbank-Spektrum: Zeitschrift Fur Datenbanktechnologie: Organ Der Fachgruppe Datenbanken Der Gesellschaft Fur Informatik e.V, 21(3), 203–212. 10.1007/s13222-021-00386-8 PubMed DOI PMC

Rahman, A., Ali, H., Badshah, N., Zakarya, M., Hussain, H., Rahman, I. U., Ahmed, A., & Haleem, M. (2022). Power mean based image segmentation in the presence of noise. Scientific Reports, 12(1), 21177. 10.1038/s41598-022-25250-x PubMed DOI PMC

Rotem-Kohavi, N., Humphreys, S., Noonan, V. K., Cheng, C. L., Guay-Paquet, M., Bouthillier, M., Valošek, J., Naga Karthik, E., Lichtenstein, E., Guenther, N., Ost, K., Attabib, N., Hardisty, M., Badhiwala, J., Larouche, J., Pahuta, M., Christie, S., Fehlings, M. G., Fourney, D. … Cadotte, D. W. (2025). Building a library of acute traumatic spinal cord injury images across Canada: A retrospective cohort study protocol. BMJ Open, 15(12), e106818. 10.1136/bmjopen-2025-106818 PubMed DOI PMC

Shi, J., & Wu, J. (2021). Distilling effective supervision for robust medical image segmentation with noisy labels. arXiv [cs.CV]. http://arxiv.org/abs/2106.11099

Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. 10.1038/s41586-024-07566-y PubMed DOI PMC

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Sodhani, S., Faramarzi, M., Mehta, S. V., Malviya, P., Abdelsalam, M., Janarthanan, J., & Chandar, S. (2022). An Introduction to lifelong supervised learning. arXiv [cs.LG]. http://arxiv.org/abs/2207.04354

Spjuth, O., Frid, J., & Hellander, A. (2021). The machine learning life cycle and the cloud: Implications for drug discovery. Expert Opinion on Drug Discovery, 16(9), 1071–1079. 10.1080/17460441.2021.1932812 PubMed DOI

Tabassam, A. I. U. (2023). MLOps: A step forward to enterprise machine learning. arXiv [cs.SE]. http://arxiv.org/abs/2305.19298

Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15(1), 29. 10.1186/s12880-015-0068-x PubMed DOI PMC

Taso, M., Girard, O. M., Duhamel, G., Le Troter, A., Feiweier, T., Guye, M., Ranjeva, J. P., & Callot, V. (2016). Tract-specific and age-related variations of the spinal cord microstructure: A multi-parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT). NMR in Biomedicine, 29(6), 817–832. 10.1002/nbm.3530 PubMed DOI

Terpilowski, M. (2019). scikit-posthocs: Pairwise multiple comparison tests in Python. Journal of Open Source Software, 4(36), 1169. 10.21105/joss.01169 DOI

Treveil, M., Omont, N., Stenac, C., Lefevre, K., Phan, D., Zentici, J., Lavoillotte, A., Miyazaki, M., & Heidmann, L. (2020). Introducing MLOps. O’Reilly Media. 10.1007/978-1-4842-9642-4_1 DOI

Tsagkas, C., Horvath-Huck, A., Haas, T., Amann, M., Todea, A., Altermatt, A., Müller, J., Cagol, A., Leimbacher, M., Barakovic, M., Weigel, M., Pezold, S., Sprenger, T., Kappos, L., Bieri, O., Granziera, C., Cattin, P., & Parmar, K. (2023). Fully automatic method for reliable spinal cord compartment segmentation in multiple sclerosis. AJNR. American Journal of Neuroradiology, 44(2), 218–227. 10.3174/ajnr.A7756 PubMed DOI PMC

Valošek, J., Bédard, S., Keřkovský, M., Rohan, T., & Cohen-Adad, J. (2024). A database of the healthy human spinal cord morphometry in the PAM50 template space. Imaging Neuroscience, 2(34), 1–15. 10.1162/imag_a_00075 PubMed DOI PMC

Valošek, J., & Cohen-Adad, J. (2024). Reproducible spinal cord quantitative MRI analysis with the Spinal Cord Toolbox. Magnetic Resonance in Medical Sciences: MRMS: An Official Journal of Japan Society of Magnetic Resonance in Medicine, 23(3), 307–315. 10.2463/mrms.rev.2023-0159 PubMed DOI PMC

van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J.-C., Pieper, S., & Aerts, H. J. W. L. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104–e107. 10.1158/0008-5472.CAN-17-0339 PubMed DOI PMC

Veiga-Canuto, D., Cerdà-Alberich, L., Sangüesa Nebot, C., Martínez de Las Heras, B., Pötschger, U., Gabelloni, M., Sierra Carot, J. M., Taschner-Mandl, S., Düster, V., Cañete, A., Ladenstein, R., Neri, E., & Martí-Bonmatí, L. (2022). Comparative multicentric evaluation of inter-observer variability in manual and automatic segmentation of neuroblastic tumors in magnetic resonance images. Cancers, 14(15), 3648. 10.3390/cancers14153648 PubMed DOI PMC

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Walt van der, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E.,…SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272. 10.1038/s41592-019-0686-2 PubMed DOI PMC

Yao, J., Zhang, Y., Zheng, S., Goswami, M., Prasanna, P., & Chen, C. (2023). Learning to segment from noisy annotations: A spatial correction approach. arXiv [eess.IV]. http://arxiv.org/abs/2308.02498