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COVID-19, a new strain of coronavirus disease, has been one of the mostserious and infectious disease in the world. Chest CT is essential inprognostication, diagnosing this disease, and assessing the complication. Inthis paper, a multi-class COVID-19 CT segmentation is proposed aiming athelping radiologists estimate the extent of effected lung volume. We utilizedfour augmented pyramid networks on an encoder-decoder segmentation framework.Quadruple Augmented Pyramid Network (QAP-Net) not only enable CNN capturefeatures from variation size of CT images, but also act as spatialinterconnections and down-sampling to transfer sufficient feature informationfor semantic segmentation. Experimental results achieve competitive performancein segmentation with the Dice of 0.8163, which outperforms otherstate-of-the-art methods, demonstrating the proposed framework can segments ofconsolidation as well as glass, ground area via COVID-19 chest CT efficientlyand accurately.

Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT