developed a classification network to differentiate between COVID-19 pneumonia and other pneumonia and achieved good performance in diagnosing the disease, achieving an area under the receiver operating characteristic of 0.95. They provided a heatmap in an effort to explain the model predictions, but it will be of great importance in disease staging and prognosis if the model can pin-point the legions accurately. This shortcoming was addressed by Zhang et al.
The work presented in this paper builds on previous research to explore the quantitative prognostication and disease staging by segmenting the COVID-19 lesions into multiple classes. Earlier work focused on segmentation using one slice in the CT at a time, whereas we focus on benefiting from additional information about the anatomy and the lesions in several adjacent slices.