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Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review

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Abstract

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression.

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Data sharing is not applicable to this article as no new data were created or analysed in this study just reviewing previous literature research.

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This publication does not have any code related to its development and no code has been published.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain

    Marcos Diaz-Hurtado, Jordi Casas-Roma & Ferran Prados

  2. Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain

    Eloy Martínez-Heras, Elisabeth Solana & Sara Llufriu

  3. Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK

    Baris Kanber & Ferran Prados

  4. National Institute for Health Research Biomedical Research Centre, University College London, London, UK

    Baris Kanber & Ferran Prados

  5. Queen Square MS Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Institute of Neurology, University College London, London, UK

    Baris Kanber & Ferran Prados

Authors
  1. Marcos Diaz-Hurtado
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  2. Eloy Martínez-Heras
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  3. Elisabeth Solana
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  4. Jordi Casas-Roma
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  5. Sara Llufriu
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  6. Baris Kanber
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  7. Ferran Prados
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M. D.-H., J. C.-R. and F.P. The first draft of the manuscript was written by M.D.-H., E. M.-H. and F.P., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Marcos Diaz-Hurtado.

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Conflict of interest

M.D.-H., E.M.-H., J.C.-R., B.K. and F.P. have nothing to disclose. E.S received travel reimbursement from Sanofi. S.L. received compensation for consulting services and speaker honoraria from Biogen Idec, Novartis, TEVA, Genzyme, Sanofi and Merck.

Ethical approval

This study has been approved by the Ethics Committee of the University Oberta de Catalunya (UOC) stating that this research does not include human subject participation or any processing of personal data and the research fulfils current legislation on data protection.

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Diaz-Hurtado, M., Martínez-Heras, E., Solana, E. et al. Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review. Neuroradiology 64, 2103–2117 (2022). https://doi.org/10.1007/s00234-022-03019-3

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