Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images

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.