Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.

中文翻译：

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用于从 COVID-19 CT 图像中分割肺部病变的有效深度神经网络


从 COVID-19 计算机断层扫描 (CT) 图像中自动分割肺部病变有助于建立诊断和治疗的定量模型。为此，本文提供了一种新的分割方法来满足COVID-19疫情下CT图像处理的需求。主要步骤如下：首先，所提出的感兴趣区域提取实施补丁机制策略以满足3D网络的适用性并去除不相关的背景。其次，建立3D网络来提取空间特征，其中3D注意力模型促进网络增强目标区域。然后，为了提高网络的收敛性，引入组合损失函数来引导梯度优化和训练方向。最后，应用数据增强和条件随机场来实现数据重采样和二进制分割。通过一些比较实验对该方法进行了评估。通过比较，所提出的方法达到了最高的性能。因此，它具有潜在的临床应用价值。