CT image segmentation of COVID-19 based on UNet++ and ResNeXt

ABSTRACT

The novel coronavirus pneumonia (COVID-19) pandemic is spreading globally. Computerized tomography (CT) imaging technology plays a vital role in the fight against global COVID-19. When diagnosing new coronary pneumonia, it will be helpful if the new coronary pneumonia focus area can be automatically and accurately segmented from the CT image The doctor makes a more accurate and quick diagnosis. Aiming at the segmentation problem of neo-coronary pneumonia lesions, an automatic segmentation method based on the improved U-Net++ model is proposed. The ResNeXt network pre-trained on ImageNet is used in the encoder and decoder to extract features of effective information. The experimental results on the public data set show. The mIou, mPA, and loss of the proposed algorithm are 81.67%, 87.78%, and 0.0145 respectively. Compared with other semantic segmentation algorithms, this method can effectively segment the neo-coronary pneumonia lesion area and has good segmentation performance. © 2021 IEEE.