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.

翻译：COVID-19是一种新的冠状病毒疾病,已成为世界上最严重的传染病之一。胸腔CT对于预测、诊断这一疾病和评估复杂程度至关重要。在本论文中,提出了多级COVID-19CT分割法,旨在帮助放射学家估计肺部影响的程度。我们利用了在编码器脱coder分解框架上的四大金字塔网络。四重增强的金字塔网络(QAP-Net)不仅使CNN能够从CT图像的变异大小中捕捉特征,而且还起到空间互联和下取样作用,以转移足够的特征信息,用于语义分解。实验结果在分解方面实现了与0.8163号骰子的竞争性性能,它超越了其他最先进的方法,表明拟议的框架能够通过COVID-19胸部CT高效和准确的组合部分以及玻璃、地面。