Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected
  Areas from Chest X-ray images
Yi Zhong Mengqiu Xu Kongming Liang Kaixin Chen Ming Wu
Segmentation of the infected areas of the lung is essential for quantifying
the severity of lung disease like pulmonary infections. Existing medical image
segmentation methods are almost uni-modal methods based on image. However,
these image-only methods tend to produce inaccurate results unless trained with
large amounts of annotated data. To overcome this challenge, we propose a
language-driven segmentation method that uses text prompt to improve to the
segmentation result. Experiments on the QaTa-COV19 dataset indicate that our
method improves the Dice score by 6.09% at least compared to the uni-modal
methods. Besides, our extended study reveals the flexibility of multi-modal
methods in terms of the information granularity of text and demonstrates that
multi-modal methods have a significant advantage over image-only methods in
terms of the size of training data required.