ArticleScientific reports2021
Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.Neurosciences (Riyadh, Saudi Arabia) · 2024Pooled it
- Transient brain structure changes after high phenylalanine exposure in adults with phenylketonuria.Brain : a journal of neurology · 2024Trial
- Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.Clinical neuroradiology · 2026Article
- Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools.Scientific reports · 2026Article
- Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL-Based Cortical Thickness Estimates.Human brain mapping · 2026Article
- Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort.Frontiers in radiology · 2026Article
- A Lesion-Aware Patch Sampling Approach with EfficientNet3D-UNet for Robust Multiple Sclerosis Lesion Segmentation.Journal of imaging · 2025Article
- Revealing morphological fingerprints in perinatal brains using quasi-conformal mapping: occurrence and neurodevelopmental implications.Brain imaging and behavior · 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
- MS-DASPNet: Multiple Sclerosis lesion segmentation from brain MRI using dual attention and spatial pyramid pooling with transfer learning.Frontiers in computational neuroscience · 2025Article
- Machine learning-based reconstruction of 2D MRI for quantitative morphometry in epilepsy.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The impact of image contrast, resolution and reader expertise on black hole identification in Multiple Sclerosis.Neuroradiology · 2024Article
- LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.medRxiv : the preprint server for health sciences · 2024Article
- BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.Human brain mapping · 2023Article
- Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis.Scientific reports · 2023Article
- Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI.Frontiers in neuroscience · 2023Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Assessment of Artificial Intelligence Automatic Multiple Sclerosis Lesion Delineation Tool for Clinical Use.Clinical neuroradiology · 2022Article
- Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.NMR in biomedicine · 2022Article
- Medical-Blocks-A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research: System Development and Integration Results.JMIR formative research · 2022Article
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Authors and funding
13 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.
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