  Deep learning methods provide significant assistance in analyzing coronavirus
disease (COVID-19) in chest computed tomography (CT) images, including
identification, severity assessment, and segmentation. Although the earlier
developed methods address the lack of data and specific annotations, the
current goal is to build a robust algorithm for clinical use, having a larger
pool of available data. With the larger datasets, the domain shift problem
arises, affecting the performance of methods on the unseen data. One of the
critical sources of domain shift in CT images is the difference in
reconstruction kernels used to generate images from the raw data (sinograms).
In this paper, we show a decrease in the COVID-19 segmentation quality of the
model trained on the smooth and tested on the sharp reconstruction kernels.
Furthermore, we compare several domain adaptation approaches to tackle the
problem, such as task-specific augmentation and unsupervised adversarial
learning. Finally, we propose the unsupervised adaptation method, called
F-Consistency, that outperforms the previous approaches. Our method exploits a
set of unlabeled CT image pairs which differ only in reconstruction kernels
within every pair. It enforces the similarity of the network hidden
representations (feature maps) by minimizing mean squared error (MSE) between
paired feature maps. We show our method achieving 0.64 Dice Score on the test
dataset with unseen sharp kernels, compared to the 0.56 Dice Score of the
baseline model. Moreover, F-Consistency scores 0.80 Dice Score between
predictions on the paired images, which almost doubles the baseline score of
0.46 and surpasses the other methods. We also show F-Consistency to better
generalize on the unseen kernels and without the specific semantic content,
e.g., presence of the COVID-19 lesions.
