SCTV-UNet: a COVID-19 CT segmentation network based on attention mechanism

The global outbreak of COVID-19 has become an important research topic in healthcare since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time is a problem. Therefore, the pathological study of COVID-19 with CT image is an important supplement to RT-RCT. The current TVLoss-based segmentation promotes the connectivity of diseased areas. However, normal pixels between some adjacent diseased areas are wrongly identified as diseased pixels. In addition, the proportion of diseased pixels in CT images is small, and the traditional BCE-based U-shaped network only focuses on the whole CT without diseased pixels, which leads to blurry border and low contrast in the predicted result. In this way, this paper proposes a SCTV-UNet to solve these problems. By combining spatial and channel attentions on the encoder, more visual layer information are obtained to recognize the normal pixels between adjacent diseased areas. By using the composite function DTVLoss that focuses on the pixels in the diseased area, the problem of blurry boundary and low contrast caused by the use of BCE in traditional U-shaped networks is solved. The experiment shows that the segmentation effect of the proposed SCTV-UNet has significantly improved by comparing with the SOTA COVID-19 segmentation networks, and can play an important role in the detection and research of clinical COVID-19.

Automated summary

The global outbreak of COVID-19 has become an important healthcare research topic since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time is an issue. Therefore, the use of CT imaging for pathological studies of COVID-19 is crucial. The proposed SCTV-UNet addresses the problems of misidentifying normal pixels and blurry boundaries in CT image segmentation, and shows improved results compared to state-of-the-art networks.