Ischemic stroke is a time-critical medical emergency where rapid diagnosis is essential for improving patient outcomes. Non-contrast computed tomography (NCCT) serves as the frontline imaging tool, yet it often fails to reveal the subtle ischemic changes present in the early, hyperacute phase. This limitation can delay crucial interventions. To address this diagnostic challenge, we introduce a semi-supervised segmentation method using generative adversarial networks (GANs) to accurately delineate early ischemic stroke regions. The proposed method employs an adversarial framework to effectively learn from a limited number of annotated NCCT scans, while simultaneously leveraging a larger pool of unlabeled scans. By employing Dice loss, cross-entropy loss, a feature matching loss and a self-training loss, the model learns to identify and delineate early infarcts, even when they are faint or their size is small. Experiments on the publicly available Acute Ischemic Stroke Dataset (AISD) demonstrate the potential of the proposed method to enhance diagnostic capabilities, reduce the burden of manual annotation, and support more efficient clinical decision-making in stroke care.

翻译：缺血性卒中是一种时间紧迫的医疗急症，快速诊断对于改善患者预后至关重要。非对比计算机断层扫描（NCCT）作为一线影像学工具，却常常难以显示早期超急性期存在的细微缺血性改变。这一局限可能延误关键干预措施。为应对这一诊断挑战，我们提出了一种基于生成对抗网络（GAN）的半监督分割方法，用于精确勾勒早期缺血性卒中区域。该方法采用对抗性框架，能够从有限数量的标注NCCT扫描中有效学习，同时利用大量未标注扫描数据。通过结合Dice损失、交叉熵损失、特征匹配损失和自训练损失，该模型能够识别并勾勒早期梗死区域，即使其表现微弱或范围较小。在公开可用的急性缺血性卒中数据集（AISD）上的实验表明，所提方法具有提升诊断能力、减轻人工标注负担以及支持卒中临床决策高效化的潜力。