  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.
