  COVID-19, a new strain of coronavirus disease, has been one of the most
serious and infectious disease in the world. Chest CT is essential in
prognostication, diagnosing this disease, and assessing the complication. In
this paper, a multi-class COVID-19 CT segmentation is proposed aiming at
helping radiologists estimate the extent of effected lung volume. We utilized
four augmented pyramid networks on an encoder-decoder segmentation framework.
Quadruple Augmented Pyramid Network (QAP-Net) not only enable CNN capture
features from variation size of CT images, but also act as spatial
interconnections and down-sampling to transfer sufficient feature information
for semantic segmentation. Experimental results achieve competitive performance
in segmentation with the Dice of 0.8163, which outperforms other
state-of-the-art methods, demonstrating the proposed framework can segments of
consolidation as well as glass, ground area via COVID-19 chest CT efficiently
and accurately.
