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) 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 to 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 model is freely available in 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 USA

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

Canada CIFAR AI Chair

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 USA

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 USA

Department of Physical Medicine and Rehabilitation University of Colorado School of Medicine Aurora CO USA

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 USA

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 UK

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 USA

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 USA

Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville USA

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

Zobrazit více v PubMed

Agirre E., Jonsson A., Larcher A., 2021. Framing lifelong learning as autonomous deployment: Tune once live forever, in: Lecture Notes in Electrical Engineering, Lecture Notes in Electrical Engineering. Springer; Singapore, Singapore, pp. 331–336.

Alla S., Adari S.K., 2020. Beginning MLOps with MLFlow: Deploy models in AWS SageMaker, Google cloud, and Microsoft azure, 1st ed. APress, Berlin, Germany.

Bautin P., Cohen-Adad J., 2021. Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants. NeuroImage Clin. 32, 102849. PubMed PMC

Bédard S., Cohen-Adad J., 2022. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Front. Neuroimaging 1, 1031253. PubMed 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. Med. Image Anal. 101, 103473. PubMed

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].

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. PubMed 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., Eippert F., Epperson K.R., Epperson K.S., Freund P., Finsterbusch J., Foias A., Fratini M., Fukunaga I., Gandini Wheeler-Kingshott C.A.M., Germani G., Gilbert G., Giove F., Gros C., Grussu F., Hagiwara A., Henry P.-G., Horák T., Hori M., Joers J., Kamiya K., Karbasforoushan H., Keřkovský M., Khatibi A., Kim J.-W., Kinany N., Kitzler H.H., Kolind S., Kong Y., Kudlička P., Kuntke P., Kurniawan N.D., Kusmia S., Labounek R., Laganà M.M., Laule C., Law C.S., Lenglet C., Leutritz T., Liu Y., Llufriu S., Mackey S., Martinez-Heras E., Mattera L., Nestrasil I., O’Grady K.P., Papinutto N., Papp D., Pareto D., Parrish T.B., Pichiecchio A., Prados F., Rovira À., Ruitenberg M.J., Samson R.S., Savini G., Seif M., Seifert A.C., Smith A.K., Smith S.A., Smith Z.A., Solana E., Suzuki Y., Tackley G., Tinnermann A., Valošek J., Van De Ville D., Yiannakas M.C., Weber K.A. Ii, Weiskopf N., Wise R.G., Wyss P.O., Xu J., 2021a. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 8, 219. PubMed 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., Eippert F., Epperson K.R., Epperson K.S., Freund P., Finsterbusch J., Foias A., Fratini M., Fukunaga I., Wheeler-Kingshott C.A.M.G., Germani G., Gilbert G., Giove F., Gros C., Grussu F., Hagiwara A., Henry P.-G., Horák T., Hori M., Joers J., Kamiya K., Karbasforoushan H., Keřkovský M., Khatibi A., Kim J.-W., Kinany N., Kitzler H., Kolind S., Kong Y., Kudlička P., Kuntke P., Kurniawan N.D., Kusmia S., Labounek R., Laganà M.M., Laule C., Law C.S., Lenglet C., Leutritz T., Liu Y., Llufriu S., Mackey S., Martinez-Heras E., Mattera L., Nestrasil I., O’Grady K.P., Papinutto N., Papp D., Pareto D., Parrish T.B., Pichiecchio A., Prados F., Rovira À., Ruitenberg M.J., Samson R.S., Savini G., Seif M., Seifert A.C., Smith A.K., Smith S.A., Smith Z.A., Solana E., Suzuki Y., Tackley G., Tinnermann A., Valošek J., Van De Ville D., Yiannakas M.C., Weber K.A. 2nd, Weiskopf N., Wise R.G., Wyss P.O., Xu J., 2021b. Generic acquisition protocol for quantitative MRI of the spinal cord. Nat. Protoc. 16, 4611–4632. PubMed 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., Mahbod A., Wang C., McKinley R., Wagner F., Muschelli J., Sweeney E., Roura E., Lladó X., Santos M.M., Santos W.P., Silva-Filho A.G., Tomas-Fernandez X., Urien H., Bloch I., Valverde S., Cabezas M., Vera-Olmos F.J., Malpica N., Guttmann C., Vukusic S., Edan G., Dojat M., Styner M., Warfield S.K., Cotton F., Barillot C., 2018. Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure. Sci. Rep. 8, 13650. PubMed PMC

De Leener B., Kadoury S., Cohen-Adad J., 2014. Robust, accurate and fast automatic segmentation of the spinal cord. Neuroimage 98, 528–536. PubMed

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, 24–43. PubMed

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. Med. Image Anal. 41, 40–54. PubMed

González C., Fuchs M., dos Santos D.P., 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].

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., Edan G., Labauge P., Callot V., Pelletier J., Audoin B., Rasoanandrianina H., Brisset J.-C., Valsasina P., Rocca M.A., Filippi M., Bakshi R., Tauhid S., Prados F., Yiannakas M., Kearney H., Ciccarelli O., Smith S., Treaba C.A., Mainero C., Lefeuvre J., Reich D.S., Nair G., Auclair V., McLaren D.G., Martin A.R., Fehlings M.G., Vahdat S., Khatibi A., Doyon J., Shepherd T., Charlson E., Narayanan S., Cohen-Adad J., 2019. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 184, 901–915. PubMed PMC

Horáková M., Horák T., Valošek J., Rohan T., Koriťáková 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. Quant. Imaging Med. Surg. 12, 2261–2279. PubMed 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. Nat. Methods 18, 203–211. PubMed

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 J. Radiol. 26, 146–155. PubMed 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. 10.1162/imag_a_00508 PubMed DOI PMC

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].

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].

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 …. Eur. Spine J. 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., Doyon J., Dvorak A.V., Eippert F., Epperson K.R., Epperson K.S., Freund P., Finsterbusch J., Foias A., Fratini M., Fukunaga I., Gandini Wheeler-Kingshott C.A.M., Germani G., Gilbert G., Giove F., Grussu F., Hagiwara A., Henry P.-G., Horák T., Hori M., Joers J.M., Kamiya K., Karbasforoushan H., Keřkovský M., Khatibi A., Kim J.-W., Kinany N., Kitzler H., Kolind S., Kong Y., Kudlička P., Kuntke P., Kurniawan N.D., Kusmia S., Laganà M.M., Laule C., Law C.S.W., Leutritz T., Liu Y., Llufriu S., Mackey S., Martin A.R., Martinez-Heras E., Mattera L., O’Grady K.P., Papinutto N., Papp D., Pareto D., Parrish T.B., Pichiecchio A., Prados F., Rovira À., Ruitenberg M.J., Samson R.S., Savini G., Seif M., Seifert A.C., Smith A.K., Smith S.A., Smith Z.A., Solana E., Suzuki Y., Tackley G.W., Tinnermann A., Valošek J., Van De Ville D., Yiannakas M.C., Weber K.A. II, Weiskopf N., Wise R.G., Wyss P.O., Xu J., Cohen-Adad J., Lenglet C., 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].

Liu B., Mazumder S., 2021. Lifelong and continual learning dialogue systems: Learning during conversation. Proc. Conf. AAAI Artif. Intell. 35, 15058–15063.

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 119 (Pt 3), 701–708. PubMed

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, 542–552. PubMed

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, e0195733. PubMed 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. J. Appl. Clin. Med. Phys. 24, e14123. PubMed PMC

Milletari F., Navab N., Ahmadi S.-A., 2016. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. arXiv [cs.CV].

Molinier N., Bédard S., Boudreau M., Cohen-Adad J., Callot V., Alonso-Ortiz E., Pageot C., Laines-Medina N., 2024. “whole-spine.” 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., 2025a. 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., 2025b. SCIseg: Automatic segmentation of intramedullary lesions in spinal cord injury on T2-weighted MRI scans. Radiol. Artif. Intell. 7, e240005. PubMed 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. Int. J. Comput. Assist. Radiol. Surg. 18, 45–54. PubMed

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. J. Neuroimaging 30, 110–118. PubMed PMC

Prapas I., Derakhshan B., Mahdiraji A.R., Markl V., 2021. Continuous training and deployment of deep learning models. Datenbank Spektrum 21, 203–212.

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. Sci. Rep. 12, 21177. PubMed PMC

Shi J., Wu J., 2021. Distilling effective supervision for robust medical image segmentation with noisy labels. arXiv [cs.CV].

Shumailov I., Shumaylov Z., Zhao Y., Papernot N., Anderson R., Gal Y., 2024. AI models collapse when trained on recursively generated data. Nature 631, 755–759. PubMed PMC

SlicerNNUnet: 3D Slicer nnUNet integration to streamline usage for nnUNet based AI extensions, n.d. Github.

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].

Tabassam A.I.U., 2023. MLOps: A step forward to enterprise machine learning. arXiv [cs.SE].

Taha A.A., Hanbury A., 2015. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 29. PubMed 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 Biomed. 29, 817–832. PubMed

Treveil M., Omont N., Stenac C., Lefevre K., Phan D., Zentici J., Lavoillotte A., Miyazaki M., Heidmann L., 2020. Introducing MLOps. O’Reilly Media, Sebastopol, CA.

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 Am. J. Neuroradiol. 44, 218–227. PubMed 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, 1–15. PubMed PMC

Valošek J., Cohen-Adad J., 2024. Reproducible spinal cord quantitative MRI analysis with the Spinal Cord Toolbox. Magn. Reson. Med. Sci. 23, 307–315. PubMed 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 Res. 77, e104–e107. PubMed 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].