At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field.

中文翻译：

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CT 图像中具有金字塔注意力和边缘损失的多类 COVID-19 分割网络。


2019年底，新型冠状病毒COVID-19爆发。由于其传染性极高，全球已有超过7400万人被感染。 CT图像中COVID-19病灶区域的自动分割是一种有效的辅助医疗技术，可以定量诊断和判断疾病的严重程度。本文提出了一种多类COVID-19 CT图像分割网络，其中包括用于提取多尺度上下文注意力信息的金字塔注意力模块和用于提高网络判别能力的残差卷积模块。还提出了小波边缘损失函数来提取病变区域的边缘特征以提高分割精度。在该实验中，构建了 4369 个 CT 切片的数据集，包括三种症状：磨玻璃样混浊、间质浸润和肺实变。模型的三个症状的骰子相似系数分别达到0.7704、0.7900、0.8241。还评估了所提出的网络在公共数据集 COVID-SemiSeg 上的性能。结果表明，该模型优于其他最先进的方法，可以成为辅助诊断阳性感染病例的有力工具，并推动医疗领域智能技术的发展。