  Segmentation of pulmonary infiltrates can help assess severity of COVID-19,
but manual segmentation is labor and time-intensive. Using neural networks to
segment pulmonary infiltrates would enable automation of this task. However,
training a 3D U-Net from computed tomography (CT) data is time- and
resource-intensive. In this work, we therefore developed and tested a solution
on how transfer learning can be used to train state-of-the-art segmentation
models on limited hardware and in shorter time. We use the recently published
RSNA International COVID-19 Open Radiology Database (RICORD) to train a fully
three-dimensional U-Net architecture using an 18-layer 3D ResNet, pretrained on
the Kinetics-400 dataset as encoder. The generalization of the model was then
tested on two openly available datasets of patients with COVID-19, who received
chest CTs (Corona Cases and MosMed datasets). Our model performed comparable to
previously published 3D U-Net architectures, achieving a mean Dice score of
0.679 on the tuning dataset, 0.648 on the Coronacases dataset and 0.405 on the
MosMed dataset. Notably, these results were achieved with shorter training time
on a single GPU with less memory available than the GPUs used in previous
studies.
