ArticleAJNR. American journal of neuroradiology2016
Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields.
Article in AJNR. American journal of neuroradiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 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
- Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge.Scientific reports · 2026Article
- 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
- Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions.Frontiers in medicine · 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
- Online hard example mining vs. fixed oversampling strategy for segmentation of new multiple sclerosis lesions from longitudinal FLAIR MRI.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
- 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
- Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks.NeuroImage. Clinical · 2020Article
- Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time.European radiology · 2019Article
- Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging.NeuroImage. Clinical · 2019Article
- Diagnostic accuracy of semiautomatic lesion detection plus quantitative susceptibility mapping in the identification of new and enhancing 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
- A novel imaging technique for better detecting new lesions in multiple sclerosis.Journal of neurology · 2017Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeDetection of disease activity, defined as new/enlarging T2 lesions on brain MR imaging, has been proposed as a biomarker in MS. However, detection of new/enlarging T2 lesions can be hindered by several factors that can be overcome with image subtraction. The purpose of this study was to improve automated detection of new T2 lesions and reduce user interaction to eliminate inter- and intraobserver variability. MATERIALS AND
methodsMultiparametric brain MR imaging was performed at 2 time points in 36 patients with new T2 lesions. Images were registered by using an affine transformation and the Demons algorithm to obtain a deformation field. After affine registration, images were subtracted and a threshold was applied to obtain a lesion mask, which was then refined by using the deformation field, intensity, and local information. This pipeline was compared with only applying a threshold, and with a state-of-the-art approach relying only on image intensities. To assess improvements, we compared the results of the different pipelines with the expert visual detection.
resultsThe multichannel pipeline based on the deformation field obtained a detection Dice similarity coefficient close to 0.70, with a false-positive detection of 17.8% and a true-positive detection of 70.9%. A statistically significant correlation (
conclusionsThe deformation field-based approach proposed in this study for detecting new/enlarging T2 lesions resulted in significantly fewer false-positives while maintaining most true-positives and showed a good correlation with visual detection annotations. This approach could reduce user interaction and inter- and intraobserver variability.
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