ReviewNeuroradiology2012
Automated detection of multiple sclerosis lesions in serial brain MRI.
Review in Neuroradiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 83 citations in OpenAlex.
- Validation of a semi-automated method to quantify lesion volume changes in multiple sclerosis on 2D proton-density-weighted scans based on image subtraction.Neuroimage. Reports · 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
- Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review.Neuroradiology · 2022Review
- Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients.Diagnostics (Basel, Switzerland) · 2022Article
- A unified framework for focal intensity change detection and deformable image registration. Application to the monitoring of multiple sclerosis lesions in longitudinal 3D brain MRI.Frontiers in neuroimaging · 2022Article
- Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions.Frontiers in medicine · 2022Article
- Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach.Frontiers in neuroscience · 2022Article
- Image registration and appearance adaptation in non-correspondent image regions for new MS lesions detection.Frontiers in neuroscience · 2022Article
- A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients.Frontiers in medicine · 2021Article
- Longitudinal analysis of white matter and cortical lesions in multiple sclerosis.NeuroImage. Clinical · 2019Article
- Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging.NeuroImage. Clinical · 2019Article
- A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation.Medical & biological engineering & computing · 2018Article
- Partial volume-aware assessment of multiple sclerosis lesions.NeuroImage. Clinical · 2018Article
- A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis.NeuroImage. Clinical · 2018Article
- Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field.AJNR. American journal of neuroradiology · 2017Article
- Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.Journal of digital imaging · 2017Review
- Neurological software tool for reliable atrophy measurement (NeuroSTREAM) of the lateral ventricles on clinical-quality T2-FLAIR MRI scans in multiple sclerosis.NeuroImage. Clinical · 2017Article
- Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields.AJNR. American journal of neuroradiology · 2016Article
- Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database.Neuroinformatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMultiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided.
methodsTime-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge.
resultsThis paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends.
conclusionLesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI.
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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.