ArticleEuropean radiology2022
Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.Neuroradiology · 2023Pooled it
- Cognition and fatigue in clinically stable multiple sclerosis: EDSS and MRI metrics outperform serum biomarkers.BMC neurology · 2026Article
- Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation.Insights into imaging · 2026Article
- A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.NeuroImage. Clinical · 2026Article
- Whole brain volume loss is associated with a short-term disability progression in relapse-activity free multiple sclerosis.Journal of neurology · 2025Observational
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.Journal of imaging informatics in medicine · 2024Article
- Consensus of algorithms for lesion segmentation in brain MRI studies of multiple sclerosis.Scientific reports · 2024Article
- Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.Scientific reports · 2024Article
- Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.European radiology · 2024Article
- Review
- AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.Insights into imaging · 2023Article
- Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment.Neuroradiology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
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Abstract
objectiveAutomated quantification of infratentorial multiple sclerosis lesions on magnetic resonance imaging is clinically relevant but challenging. To overcome some of these problems, we propose a fully automated lesion segmentation algorithm using 3D convolutional neural networks (CNNs).
methodsThe CNN was trained on a FLAIR image alone or on FLAIR and T1-weighted images from 1809 patients acquired on 156 different scanners. An additional training using an extra class for infratentorial lesions was implemented. Three experienced raters manually annotated three datasets from 123 MS patients from different scanners.
resultsThe inter-rater sensitivity (SEN) was 80% for supratentorial lesions but only 62% for infratentorial lesions. There was no statistically significant difference between the inter-rater SEN and the SEN of the CNN with respect to the raters. For supratentorial lesions, the CNN featured an intra-rater intra-scanner SEN of 0.97 (R1 = 0.90, R2 = 0.84) and for infratentorial lesion a SEN of 0.93 (R1 = 0.61, R2 = 0.73).
conclusionThe performance of the CNN improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input and matches the detection performance of experienced raters. Furthermore, for infratentorial lesions the CNN was more robust against repeated scans than experienced raters. KEY POINTS: • A 3D convolutional neural network was trained on MRI data from 1809 patients (156 different scanners) for the quantification of supratentorial and infratentorial multiple sclerosis lesions. • Inter-rater variability was higher for infratentorial lesions than for supratentorial lesions. The performance of the 3D convolutional neural network (CNN) improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input. • The detection performance of the CNN matches the detection performance of experienced raters.
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