  Quantitative lung measures derived from computed tomography (CT) have been
demonstrated to improve prognostication in coronavirus disease (COVID-19)
patients, but are not part of the clinical routine since required manual
segmentation of lung lesions is prohibitively time-consuming. We propose a new
fully automated deep learning framework for rapid quantification and
differentiation between lung lesions in COVID-19 pneumonia from both contrast
and non-contrast CT images using convolutional Long Short-Term Memory
(ConvLSTM) networks. Utilizing the expert annotations, model training was
performed 5 times with separate hold-out sets using 5-fold cross-validation to
segment ground-glass opacity and high opacity (including consolidation and
pleural effusion). The performance of the method was evaluated on CT data sets
from 197 patients with positive reverse transcription polymerase chain reaction
test result for SARS-CoV-2. Strong agreement between expert manual and
automatic segmentation was obtained for lung lesions with a Dice score
coefficient of 0.876 $\pm$ 0.005; excellent correlations of 0.978 and 0.981 for
ground-glass opacity and high opacity volumes. In the external validation set
of 67 patients, there was dice score coefficient of 0.767 $\pm$ 0.009 as well
as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high
opacity volumes. Computations for a CT scan comprising 120 slices were
performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX
graphics processing unit. Therefore, our deep learning-based method allows
rapid fully-automated quantitative measurement of pneumonia burden from CT and
may generate results with an accuracy similar to the expert readers.
