标题： 临床信息预处理在低资源环境中改善中风分割 显示英文标题

标题： Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings

摘要： 中风是全球三大死亡原因之一，从影像中准确识别缺血性中风病灶边界对于诊断和治疗至关重要。 主要的影像学检查方法包括磁共振成像（MRI），特别是扩散加权成像（DWI），以及基于计算机断层扫描（CT）的技术，如非增强CT（NCCT）、对比增强CT血管造影（CTA）和CT灌注（CTP）。 DWI是病灶识别的金标准，但由于成本高昂，在资源匮乏的环境中应用受限。 基于CT的影像学检查目前在资源匮乏的环境中是最实用的影像学方法，因为成本低且物流简化，但在监测缺血损伤方面缺乏MRI方法的高特异性。 监督深度学习方法是自动缺血性中风病灶分割的领先解决方案，并通过在分割CT时结合DWI的见解，为资源匮乏环境中提高诊断质量提供了机会。 在这里，我们开发了一系列模型，使用到达时拍摄的CT图像作为输入，预测后续从2-9天后拍摄的DWI标注的病灶体积。 此外，我们实施了临床动机驱动的预处理步骤，并表明与使用基础预处理训练的nnU-Net模型相比，所提出的流程在10折中Dice分数提高了38%。 最后，我们证明通过进一步对CTA图进行预处理以提取血管分割，我们在5折中进一步将最佳模型提升了21%。 显示英文摘要

摘要： Stroke is among the top three causes of death worldwide, and accurate identification of ischemic stroke lesion boundaries from imaging is critical for diagnosis and treatment. The main imaging modalities used include magnetic resonance imaging (MRI), particularly diffusion weighted imaging (DWI), and computed tomography (CT)-based techniques such as non-contrast CT (NCCT), contrast-enhanced CT angiography (CTA), and CT perfusion (CTP). DWI is the gold standard for the identification of lesions but has limited applicability in low-resource settings due to prohibitive costs. CT-based imaging is currently the most practical imaging method in low-resource settings due to low costs and simplified logistics, but lacks the high specificity of MRI-based methods in monitoring ischemic insults. Supervised deep learning methods are the leading solution for automated ischemic stroke lesion segmentation and provide an opportunity to improve diagnostic quality in low-resource settings by incorporating insights from DWI when segmenting from CT. Here, we develop a series of models which use CT images taken upon arrival as inputs to predict follow-up lesion volumes annotated from DWI taken 2-9 days later. Furthermore, we implement clinically motivated preprocessing steps and show that the proposed pipeline results in a 38% improvement in Dice score over 10 folds compared to a nnU-Net model trained with the baseline preprocessing. Finally, we demonstrate that through additional preprocessing of CTA maps to extract vessel segmentations, we further improve our best model by 21% over 5 folds.