In this paper, a progressive global perception and local polishing (PCPLP) network is proposed to automatically segment the COVID-19-caused pneumonia infections in computed tomography (CT) images. The proposed PCPLP follows an encoder-decoder architecture. Particularly, the encoder is implemented as a computationally efficient fully convolutional network (FCN). In this study, a multi-scale multi-level feature recursive aggregation (mmFRA) network is used to integrate multi-scale features (viz. global guidance features and local refinement features) with multi-level features (viz. high-level semantic features, middle-level comprehensive features, and low-level detailed features). Because of this innovative aggregation of features, an edge-preserving segmentation map can be produced in a boundary-aware multiple supervision (BMS) way. Furthermore, both global perception and local perception are devised. On the one hand, a global perception module (GPM) providing a holistic estimation of potential lung infection regions is employed to capture more complementary coarse-structure information from different pyramid levels by enlarging the receptive fields without substantially increasing the computational burden. On the other hand, a local polishing module (LPM), which provides a fine prediction of the segmentation regions, is applied to explicitly heighten the fine-detail information and reduce the dilution effect of boundary knowledge. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed PCPLP in boosting the learning ability to identify the lung infected regions with clear contours accurately. Our model is superior remarkably to the state-of-the-art segmentation models both quantitatively and qualitatively on a real CT dataset of COVID-19.

在本文中，提出了一种渐进式全局感知和局部抛光（PCPLP）网络，以自动分割计算机断层扫描（CT）图像中由 COVID-19 引起的肺炎感染。提议的 PCPLP 遵循编码器-解码器架构。特别是，编码器被实现为计算高效的全卷积网络（FCN）。在这项研究中，多尺度多层次特征递归聚合(mmFRA) 网络用于将多尺度特征（即全局引导特征和局部细化特征）与多层次特征（即高级语义特征、中级综合特征和低级详细特征）集成. 由于这种创新的特征聚合，可以以边界感知多重监督(BMS) 的方式生成边缘保留分割图。此外，还设计了全局感知和局部感知。一方面，全局感知模块(GPM) 提供对潜在肺部感染区域的整体估计，用于通过扩大感受野而不显着增加计算负担来从不同的金字塔级别捕获更多互补的粗结构信息。另一方面，本地抛光模块（LPM）提供了分割区域的精细预测，用于显式增强精细信息并减少边界知识的稀释效应。综合实验评估证明了所提出的 PCPLP 在提高学习能力以准确识别轮廓清晰的肺部感染区域方面的有效性。我们的模型在 COVID-19 的真实 CT 数据集上在数量和质量上都明显优于最先进的分割模型。