  Deep neural networks have demonstrated exceptional efficacy in stroke lesion
segmentation. However, the delineation of small lesions, critical for stroke
diagnosis, remains a challenge. In this study, we propose two straightforward
yet powerful approaches that can be seamlessly integrated into a variety of
networks: Multi-Size Labeling (MSL) and Distance-Based Labeling (DBL), with the
aim of enhancing the segmentation accuracy of small lesions. MSL divides lesion
masks into various categories based on lesion volume while DBL emphasizes the
lesion boundaries. Experimental evaluations on the Anatomical Tracings of
Lesions After Stroke (ATLAS) v2.0 dataset showcase that an ensemble of MSL and
DBL achieves consistently better or equal performance on recall (3.6% and
3.7%), F1 (2.4% and 1.5%), and Dice scores (1.3% and 0.0%) compared to the
top-1 winner of the 2022 MICCAI ATLAS Challenge on both the subset only
containing small lesions and the entire dataset, respectively. Notably, on the
mini-lesion subset, a single MSL model surpasses the previous best ensemble
strategy, with enhancements of 1.0% and 0.3% on F1 and Dice scores,
respectively. Our code is available at:
https://github.com/nadluru/StrokeLesSeg.
