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Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread andposed a serious threat to public health. Chest Computed Tomography (CT) holdsgreat potential for screening and diagnosis of this disease. The segmentationof COVID-19 CT imaging can achieves quantitative evaluation of infections andtracks disease progression. COVID-19 infections are characterized by highheterogeneity and unclear boundaries, so capturing low-level features such astexture and intensity is critical for segmentation. However, segmentationnetworks that emphasize low-level features are still lacking. In this work, wepropose a DECOR-Net capable of capturing more decorrelated low-level features.The channel re-weighting strategy is applied to obtain plenty of low-levelfeatures and the dependencies between channels are reduced by proposeddecorrelation loss. Experiments show that DECOR-Net outperforms othercutting-edge methods and surpasses the baseline by 5.1% and 4.9% in terms ofDice coefficient and intersection over union. Moreover, the proposeddecorrelation loss can improve the performance constantly under differentsettings. The Code is available at https://github.com/jiesihu/DECOR-Net.git.

DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features