ArticleScientific reports2023
Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A multi-sequence MRI integration framework using SwinUNETR-v2 for multiple sclerosis lesion segmentation.BMC medical informatics and decision making · 2026Article
- RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.Scientific reports · 2025Article
- A Lesion-Aware Patch Sampling Approach with EfficientNet3D-UNet for Robust Multiple Sclerosis Lesion Segmentation.Journal of imaging · 2025Article
- Assessing the role of volumetric brain information in multiple sclerosis progression.Computational and structural biotechnology journal · 2025Article
- Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation.NeuroImage. Clinical · 2025Article
- Consensus of algorithms for lesion segmentation in brain MRI studies of multiple sclerosis.Scientific reports · 2024Article
- LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.medRxiv : the preprint server for health sciences · 2024Article
- Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.Neuroradiology · 2024Article
- A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation.Journal of neuroimaging : official journal of the American Society of NeuroimagingReview
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Authors and funding
6 authors.
Funding
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Abstract
T2 lesion quantification plays a crucial role in monitoring disease progression and evaluating treatment response in multiple sclerosis (MS). We developed a 3D, multi-arm U-Net for T2 lesion segmentation, which was trained on a large, multicenter clinical trial dataset of relapsing MS. We investigated its generalization to other relapsing and primary progressive MS clinical trial datasets, and to an external dataset from the MICCAI 2016 MS lesion segmentation challenge. Additionally, we assessed the model's ability to reproduce the separation of T2 lesion volumes between treatment and control arms; and the association of baseline T2 lesion volumes with clinical disability scores compared with manual lesion annotations. The trained model achieved a mean dice coefficient of ≥ 0.66 and a lesion detection sensitivity of ≥ 0.72 across the internal test datasets. On the external test dataset, the model achieved a mean dice coefficient of 0.62, which is comparable to 0.59 from the best model in the challenge, and a lesion detection sensitivity of 0.68. Lesion detection performance was reduced for smaller lesions (≤ 30 μL, 3-10 voxels). The model successfully maintained the separation of the longitudinal changes in T2 lesion volumes between the treatment and control arms. Such tools could facilitate semi-automated MS lesion quantification; and reduce rater burden in clinical trials.
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Registered trials
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