  Chest computed tomography (CT) has played an essential diagnostic role in
assessing patients with COVID-19 by showing disease-specific image features
such as ground-glass opacity and consolidation. Image segmentation methods have
proven to help quantify the disease burden and even help predict the outcome.
The availability of longitudinal CT series may also result in an efficient and
effective method to reliably assess the progression of COVID-19, monitor the
healing process and the response to different therapeutic strategies. In this
paper, we propose a new framework to identify infection at a voxel level
(identification of healthy lung, consolidation, and ground-glass opacity) and
visualize the progression of COVID-19 using sequential low-dose non-contrast CT
scans. In particular, we devise a longitudinal segmentation network that
utilizes the reference scan information to improve the performance of disease
identification. Experimental results on a clinical longitudinal dataset
collected in our institution show the effectiveness of the proposed method
compared to the static deep neural networks for disease quantification.
