ArticleNeuroImage. Clinical2018
A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.Journal of imaging informatics in medicine · 2025Pooled it
- Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Review
- Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation.NeuroImage. Clinical · 2025Article
- The bi-phasic behaviour of grey matter networks after the first demyelinating attack.Brain communications · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks.European radiology · 2022Article
- Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions.Frontiers 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
- Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach.Frontiers in neuroscience · 2022Article
- Longitudinal detection of new MS lesions using deep learning.Frontiers in neuroimaging · 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
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients.Frontiers in medicine · 2021Article
- Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images.Frontiers in immunology · 2021Review
- Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends.Patterns (New York, N.Y.) · 2020Review
- Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions.European radiology · 2020Article
- MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.Journal of digital imaging · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLongitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using features from image intensities, subtraction values, and deformation fields (DF).
methodsOne year apart multi-channel brain MRI scans were obtained for 60 patients, 36 of them with new T2-w lesions. Images from both temporal points were preprocessed and co-registered. Afterwards, they were registered using multi-resolution affine registration, allowing their subtraction. In particular, the DFs between both images were computed with the Demons non-rigid registration algorithm. Afterwards, a logistic regression model was trained with features from image intensities, subtraction values, and DF operators. We evaluated the performance of the model following a leave-one-out cross-validation scheme.
resultsIn terms of detection, we obtained a mean Dice similarity coefficient of 0.77 with a true-positive rate of 74.30% and a false-positive detection rate of 11.86%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.56. The performance of our model was significantly higher than state-of-the-art methods.
conclusionsThe performance of the proposed method shows the benefits of using DF operators as features to train a supervised learning model. Compared to other methods, the proposed model decreases the number of false-positives while increasing the number of true-positives, which is relevant for clinical settings.
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