# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
###### Abstract  
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's hidden representations (feature maps) by minimizing the mean squared error (MSE) between paired feature maps. We show our method achieving a 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 presence of the COVID-19 lesions than the other methods trained on unlabeled data. c Date of the IEEE Computer Society, Vol. 1, No. segmentation masks. To achieve the high segmentation quality, more sophisticated methods have been designed, e.g., solving a multitask problem and merging datasets with different annotations [8]. Now, a larger pool of COVID-19 segmentation datasets is available, e.g., [9]; therefore, the current goal is to build a robust algorithm for clinical use. In merging a larger pool of data, the problem of _domain shift_ arises. Domain shift is one of the most salient problems in medical computer vision [10]. A model trained on the data from one distribution might yield poor results on the data from a different distribution. In CT imaging, one of the main sources of the domain shift is the difference in _reconstruction kernels_. Here, the reconstruction kernel is a parameter of the Filtered Back Projection (FBP) reconstruction algorithm [11]. One could perceptually compare the same image reconstructed with two different kernels in Figure 3, e.g., B1 and B2. For the kernel-caused domain shift, several works have shown the deterioration of the quality of the model in lung cancer segmentation [12] and in emphysema segmentation [13]. In this paper, we show that the domain shift induced by the difference in reconstruction kernels decreases the quality of the COVID-19 segmentation algorithms. To do so, we construct two domains from the publicly available data: the _source_ domain with the _smooth_ reconstruction kernels and _target_ domain with the _sharp_ reconstruction kernels. We train the segmentation model on the source domain and test it on the target domain. We then validate the most relevant domain adaptation methods with the observed decrease in test scores. In our comparison, we include an augmentation approach [14], unsupervised adversarial learning [15], and also our proposed method that couples with the considered domain shift problem more efficiently. All methods except the augmentation one require additional unlabeled data from the _target_ domain. In this task, a large pool of unlabeled chest CT image pairs which differ only in reconstruction kernels within every pair is publicly available, e.g., [16].