ReviewNeuroradiology2022
Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review.
Review in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
- Artificial Intelligence for Gadolinium-Sparing Detection of Active Multiple Sclerosis Lesions: A Structured Narrative Review.Neurology and therapy · 2026Review
- Manual volumetric segmentation of brain hyperintensities on T2-weighted imaging (T2WI) and fluid-attenuated inversion recovery (FLAIR) sequences in children with neurofibromatosis type 1: inter-rater reliability and clinical correlates.Quantitative imaging in medicine and surgery · 2026Article
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- Microstructure-informed deep learning improves thalamic atrophy segmentation and clinical associations in multiple sclerosis and related neuroimmunological diseases.NeuroImage. Clinical · 2026Article
- Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort.Frontiers in radiology · 2026Article
- Evolving the Diagnosis of Multiple Sclerosis: A New Landscape in Light of the 2024 McDonald Criteria.Biomedicines · 2025Review
- Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments.AJNR. American journal of neuroradiology · 2025Article
- Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI.Scientific reports · 2024Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.medRxiv : the preprint server for health sciences · 2024Article
- A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.AJNR. American journal of neuroradiology · 2024Article
- Evaluation of the Statistical Detection of Change Algorithm for Screening Patients with MS with New Lesion Activity on Longitudinal Brain MRI.AJNR. American journal of neuroradiology · 2023Article
- Deep Learning Aided Neuroimaging and Brain Regulation.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.