Purpose: Quantitative lung measures derived from computed tomography (CT) have been
demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients
but are not part of clinical routine because the required manual segmentation of lung
lesions is prohibitively time consuming. We aim to automatically segment ground-glass
opacities and high opacities (comprising consolidation and pleural effusion). Approach:
We propose a new fully automated deep-learning framework for fast multi-class segmentation
of 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 using five-fold cross-validation to segment
COVID-19 lesions. The performance of the method was evaluated on CT datasets from
197 patients with a positive reverse transcription polymerase chain reaction test
result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results:
Strong agreement between expert manual and automatic segmentation was obtained for
lung lesions with a Dice score of 0.89±0.07 ; excellent correlations of 0.93 and 0.98
for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained.
In the external testing set of 68 patients, we observed a Dice score of 0.89±0.06
as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes,
respectively. Computations for a CT scan comprising 120 slices were performed under
3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated
quantification of the lung burden % discriminate COVID-19 patients from controls with
an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method
allows for the rapid fully automated quantitative measurement of the pneumonia burden
from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on
chest CT.
