Pairwise hemorrhage-brain region interaction-driven hemorrhagic stroke assessment in CT

<i>Objective.</i>Hemorrhagic stroke is a major global health problem requiring rapid and accurate diagnosis for effective treatment. Despite advances, current computer-aided diagnosis (CAD) frameworks rarely account for the functional impacts of hemorrhages on specific critical brain regions and lack the detailed assessments essential for precise treatment. To provide detailed insights into hemorrhages, we aim to propose a CAD framework for in-depth hemorrhagic stroke assessment in computed tomography (CT). The framework includes segmenting hemorrhages and critical brain regions, intraparenchymal hemorrhage (IPH) classification for identifying hemorrhages in critical brain regions and detecting hemorrhage volume.<i>Approach.</i>To capture the complex interactions between hemorrhages and critical brain regions, we developed the pairwise hemorrhage-brain regions interaction (PHRI) Network. Its emphasis a novel interaction head that integrates feature on hemorrhages, brain regions, and their interdependencies, enabling the model to learn these relationships during training. In addition, a Global-Local Fusion Unit was introduced to provide with image-wide contextual information, and an uncertainty-weighted loss method was utilized to simultaneously optimise the multitask framework. With institutional review board approval, an in-house hemorrhagic stroke dataset was collected, including 2,764 CT slices from 99 patients. A five-fold cross-validation was used to train and test the models.<i>Main Results.</i>The proposed PHRI network was experimentally validated to effectively extract hemorrhage brain region interactions, thereby significantly outperforming several state-of-the-art models in both hemorrhage and critical brain region segmentation, with an average Dice of 0.9064 ± 0.1079 (<i>P</i><0.05), as well as in IPH classification, with an F1-Score of 0.8366. Additionally, the framework demonstrated good performance in hemorrhage volume detection, with an intraclass correlation coefficient of 0.981.<i>Significance.</i>This study introduces a CAD framework for hemorrhagic stroke assessment, offering a novel approach that emphasizes relationships between hemorrhages and critical brain regions.