Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed information and high-quality annotation in the COVID-19 dataset. To solve the above problem, we propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information containing the number of lesions and specific locations of image information. The introduction of text information allows the network to achieve better prediction results on challenging datasets. We conduct extensive experiments on two COVID-19 datasets including chest X-ray and CT, and the results demonstrate that our proposed method outperforms other state-of-the-art segmentation methods.

翻译：然而,由于COVID-19数据集缺乏详细的资料和高质量的说明,只有少数相关研究可以帮助医生更好地诊断和治疗COVID-19。为了解决上述问题,我们提议C2FVL,即通过Vision-Language校正的粗略至法系分割框架,合并含有损害数量和图像信息具体位置的文本信息。引入文本信息使网络能够在具有挑战性的数据集上取得更好的预测结果。我们广泛试验了两个COVID-19数据集,包括胸腔X光和CT,结果显示我们拟议的方法优于其他最先进的分割方法。</s>