ArXiv Preprint
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.
Keno K. Bressem, Stefan M. Niehues, Bernd Hamm, Marcus R. Makowski, Janis L. Vahldiek, Lisa C. Adams
2021-01-25
