Enhancing infarct segmentation performance using domain-specific attention in acute ischemic stroke

Ischemic stroke infarct tissues are not salvageable. The infarct volume calculated from a segmented infarct region is an important parameter required to decide on the optimal treatment workflow. Deep learning continues to demonstrate the significance of end-to-end training with limited use of apriori knowledge (such as domain-aware feature engineering) in learning medical imaging tasks. Incorporating prior domain-specific knowledge introduces better inductive bias in learning tasks with low data availability, thereby improving performance. Several techniques have been used for segmentation of infarct region ranging from traditional approaches like region growing to deep learning approaches with limited use of domain-specific knowledge. This paper incorporates domain-specific knowledge into deep neural networks to restrict the region of interest thereby improving the performance of infarct segmentation. Incorporating domain-specific knowledge improve the performance by 17%.