Lightweight Multi-Modal Attention U-Net for Micro-Lesion Segmentation in MS
Year: 2026
Type: article
Abstract: Accurate detection of micro-lesions in Multiple Sclerosis (MS) MRI remains a persistent clinical challenge due to extreme class imbalance, low boundary contrast, and the computational demands of existing volumetric deep learning frameworks that limit their deployment in resource-constrained environments. This study proposes a lightweight multi-modal attention U-Net specifically optimized for micro-lesion segmentation, targeting lesion instances with volumetric extent smaller than 10 mm³. The proposed architecture employs a 2D encoder-decoder backbone augmented with micro-lesion-scale attention gates that selectively fuse FLAIR and T1-weighted feature representations, restricting the effective receptive field to sub-centimeter regions to suppress background activations and enhance small lesion sensitivity. Training is guided by a composite loss combining Dice loss, micro-lesion class weighting, and a boundary-sensitive distance transform term, supported by an enriched batch sampling strategy that ensures adequate micro-lesion representation throughout optimization. Evaluation on three publicly available benchmarks — ISBI 2015, MICCAI 2016, and 3D-MR-MS — demonstrates that the proposed model achieves a Dice Similarity Coefficient of 74.6% on the micro-lesion subset, representing a 10.8% improvement over the standard U-Net baseline, while operating at 6.3× faster inference speed and requiring 8.7× fewer parameters than Trans-U-Net++. These results confirm that task-specific lightweight design yields a clinically meaningful accuracy-efficiency trade-off, rendering the proposed framework suitable for real-time deployment in routine MS assessment pipelines.
Authors: hanan soliman, Mahmoud Hussein, Hatem Mohamed, asmaa elsaid
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7208817825