Stroke Lesion Segmentation from Low-Quality and Few-Shot MRIs via Similarity-Weighted Self-ensembling Framework

Ischemic stroke lesion is one of the prevailing diseases with the highest mortality in low- and middle-income countries. Although deep learning-based segmentation methods have the great potential to improve the medical resource imbalance and reduce stroke risk in these countries, existing segmentation studies are difficult to be deployed in these low-resource settings because they have such high requirements for the data amount (plenty-shot) and quality (high-field and high resolution) that are usually unavailable in these countries. In this paper, we propose a SimIlarity-weiGhed self-eNsembling framework (SIGN) to segment stroke lesions from low-quality and few-shot MRI data by leveraging publicly available glioma data. To overcome the low-quality challenge, a novel Identify-to-Discern Network employs attention mechanisms to identify lesions from a global perspective and progressively refine the coarse prediction via focusing on the ambiguous regions. To overcome the few-shot challenge, a new Soft Distribution-aware Updating strategy trains the Identify-to-Discern Network in the direction beneficial to tumor segmentation via respective optimizing schemes and adaptive similarity evaluation on glioma and stroke data. The experiment indicates our method outperforms existing few-shot methods and achieves the Dice of 76.84% after training with 14-case low-quality stroke lesion data, illustrating the effectiveness of our method and the potential to be deployed in low resource settings. Code is available in: https://github.com/MINDLABI/SIGN.