Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2956-2959. doi: 10.1109/EMBC46164.2021.9629904.
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 inter-connections 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 segment of consolidation as well as glass, ground area via COVID-19 chest CT efficiently and accurately.
COVID-19，一种新型冠状病毒病，是世界上最严重和最具传染性的疾病之一。胸部 CT 对于预后、诊断该疾病和评估并发症至关重要。在本文中，提出了一种多类 COVID-19 CT 分割方法，旨在帮助放射科医生估计受影响肺体积的程度。我们在编码器-解码器分割框架上使用了四个增强金字塔网络。四重增强金字塔网络（QAP-Net）不仅使 CNN 能够从 CT 图像的不同大小捕获特征，而且还充当空间连接和下采样，以传输用于语义分割的足够特征信息。实验结果在分割方面表现出具有竞争力的性能，Dice 系数为 0.8163，优于其他最先进的方法，表明所提出的框架可以有效地和准确地对 COVID-19 胸部 CT 的实变区以及磨玻璃影、ground area 进行分割。
