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Segmentation of pulmonary infiltrates can help assess severity of COVID-19,but manual segmentation is labor and time-intensive. Using neural networks tosegment pulmonary infiltrates would enable automation of this task. However,training a 3D U-Net from computed tomography (CT) data is time- andresource-intensive. In this work, we therefore developed and tested a solutionon how transfer learning can be used to train state-of-the-art segmentationmodels on limited hardware and in shorter time. We use the recently publishedRSNA International COVID-19 Open Radiology Database (RICORD) to train a fullythree-dimensional U-Net architecture using an 18-layer 3D ResNet, pretrained onthe Kinetics-400 dataset as encoder. The generalization of the model was thentested on two openly available datasets of patients with COVID-19, who receivedchest CTs (Corona Cases and MosMed datasets). Our model performed comparable topreviously published 3D U-Net architectures, achieving a mean Dice score of0.679 on the tuning dataset, 0.648 on the Coronacases dataset and 0.405 on theMosMed dataset. Notably, these results were achieved with shorter training timeon a single GPU with less memory available than the GPUs used in previousstudies.

3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware