ArticleNeuroradiology2025
A multi-center study of transformer-based CNNs for multiple sclerosis lesion segmentation on 3D FLAIR MRI.
Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification.Frontiers in neurologyArticle
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13 authors.
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Abstract
objectiveThis study aimed to develop and evaluate a Transformer-CNN framework for automated segmentation of multiple sclerosis (MS) lesions on FLAIR MRI. The model was benchmarked against U-Net and DeepLabV3 and assessed for both segmentation accuracy and across-center performance under internal 5-fold cross-validation to ensure robustness across diverse clinical datasets. MATERIALS AND
methodsA dataset of 1,800 3D FLAIR MRI scans from five clinical centers was split using 5-fold cross-validation. Preprocessing included isotropic resampling, intensity normalization, and bias field correction. The Transformer-CNN combined CNN-based local feature extraction with Transformer-based global context modeling. Data augmentation strategies, including geometric transformations and noise injection, enhanced generalization. Performance was evaluated using Dice score, IoU, HD95, and pixel accuracy, along with internal cross-validation-based metrics such as Generalized Dice Similarity Coefficient (GDSC), Domain-wise IoU (DwIoU), Cross-Fold Dice Deviation (CFDD), and Volume Agreement (Intraclass Correlation Coefficient, ICC). Statistical significance was tested using Kruskal-Wallis and Dunn's post-hoc analyses to compare models.
resultsThe Transformer-CNN achieved the best overall performance, with a Dice score of 92.3%, IoU of 91.4%, HD95 of 2.25 mm, and pixel accuracy of 95.6%. It also excelled in internal cross-validation-based across-center metrics, achieving the highest GDSC (91.3%) and DwIoU (89.2%), the lowest CFDD (1.05%), and the highest ICC (96.5%). DeepLabV3 and U-Net scored 85.1% and 83.0% in Dice, with HD95 values of 4.15 mm and 4.30 mm, respectively. The worst performance was observed in U-Net, which exhibited high variability across datasets and struggled with small lesion detection.
conclusionsThe Transformer-CNN outperformed U-Net and DeepLabV3 in segmentation accuracy and across-center performance under internal 5-fold cross-validation. Its robustness, minimal variability, and ability to generalize across diverse datasets establish it as a practical and reliable tool for clinical MS lesion segmentation and monitoring.
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