Stroke is the second leading cause of death worldwide, and is increasingly prevalent in low- and middle-income countries (LMICs). Timely interventions can significantly influence stroke survivability and the quality of life after treatment. However, the standard and most widely available imaging method for confirming strokes and their sub-types, the NCCT, is more challenging and time-consuming to employ in cases of ischemic stroke. For this reason, we developed an automated method for ischemic stroke lesion segmentation in NCCTs using the nnU-Net frame work, aimed at enhancing early treatment and improving the prognosis of ischemic stroke patients. We achieved Dice scores of 0.596 and Intersection over Union (IoU) scores of 0.501 on the sampled dataset. After adjusting for outliers, these scores improved to 0.752 for the Dice score and 0.643 for the IoU. Proper delineation of the region of infarction can help clinicians better assess the potential impact of the infarction, and guide treatment procedures.

翻译：脑卒中是全球第二大死亡原因，在中低收入国家日益普遍。及时干预能显著影响卒中患者的存活率和治疗后生活质量。然而，用于确认卒中及其亚型的标准且最广泛应用的影像学方法——非对比计算机断层扫描（NCCT），在缺血性脑卒中病例中的应用更具挑战性且耗时。为此，我们开发了一种基于nnU-Net框架的NCCT图像中缺血性脑卒中病灶自动分割方法，旨在促进早期治疗并改善缺血性脑卒中患者的预后。在采样数据集上，我们获得了0.596的Dice分数和0.501的交并比（IoU）分数。经异常值调整后，Dice分数提升至0.752，IoU分数提升至0.643。准确勾画梗死区域有助于临床医生更好地评估梗死的潜在影响，并指导治疗流程。