ArticleNeuroImage. Clinical2020
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.
Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.
- A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.Journal of imaging informatics in medicine · 2025Pooled it
- Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.Neurosciences (Riyadh, Saudi Arabia) · 2024Pooled it
- Effects of HLA-DRA, HLA-DQA1 and IL-6 Gene Variations to Glatiramer Acetate Resistance in Multiple Sclerosis Patients.Biochemical genetics · 2026Article
- BrainAgeMS: protocol for a prospective comparative study of brain aging in healthy adults and people with multiple sclerosis.Frontiers in neurology · 2026Observational
- Prospective clinical evaluation of automatic lesion assessment in patients with multiple sclerosis.Neuroradiology · 2025Article
- Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors.ArXiv · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.Frontiers in neuroscience · 2023Article
- Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques.International journal of molecular sciences · 2022Article
- The current role and future directions of imaging in failed back surgery syndrome patients: an educational review.Insights into imaging · 2022Article
- A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.Neuroradiology · 2022Article
- Predictive MRI Biomarkers in MS-A Critical Review.Medicina (Kaunas, Lithuania) · 2022Review
- New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation.Frontiers in neuroscience · 2022Article
- Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.Computational and mathematical methods in medicine · 2022Article
- New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images.Frontiers in neuroscience · 2022Article
- Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies.Frontiers in neuroscience · 2022Article
- Image registration and appearance adaptation in non-correspondent image regions for new MS lesions detection.Frontiers in neuroscience · 2022Article
- Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
19 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points.
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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.