Plug and Play Labeling Strategies for Boosting Small Brain Lesion Segmentation.

Accurate segmentation of small brain lesions in magnetic resonance imaging (MRI) is essential for understanding neurological disorders and guiding clinical decisions. However, detecting small lesions remains challenging due to low contrast and limited size. This study proposes two simple yet effective labeling strategies, Multi-Size Labeling (MSL) and Distance-Based Labeling (DBL), that can seamlessly integrate into existing segmentation networks. MSL groups lesions based on volume to enable size-aware learning, while DBL emphasizes lesion boundaries to enhance structural sensitivity. We evaluate our approach on two benchmark datasets: stroke lesion segmentation using the Anatomical Tracings of Lesions After Stroke (ATLAS) v2.0 dataset and multiple sclerosis lesion segmentation using the MSLesSeg dataset. On ATLAS v2.0, our approach achieved higher Dice (+1.3%), F1 (+2.4%), precision (+7.2%), and recall (+3.6%) scores compared to the top-performing method from a previous challenge. On MSLesSeg, our approach achieved the highest Dice score (0.7146) and ranked first among 16 international teams. Additionally, we examined the effectiveness of attention-based and mamba-based segmentation models but found that our proposed labeling strategies yielded more consistent improvements. These findings demonstrate that MSL and DBL offer a robust and generalizable solution for enhancing small brain lesion segmentation across various tasks and architectures. Our code is available at: https://github.com/nadluru/StrokeLesSeg.