# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
## 5 Results
### Methods Comparison  
To begin with, we show the existence of the domain shift problem within the COVID-19 segmentation task. The Dice Score of the baseline model on the COVID-test dataset is lower than the cross-validation score on the COVID-train dataset, 0.56 against 0.60. Also, this score is significantly lower than 0.64 achieved by our adaptation method \((p_{1}<0.05,\ p_{2}<10^{-4})\). Results could be found in Table 3 comparing row Baseline to the others. For all adaptation methods, we observe an increase in the consistency score and segmentation quality on the target domain. Moreover, all methods maintain their quality on the source domain compared to Baseline.  
Further, we compare FBPAug to the best adaptation methods since it is a straightforward solution to the domain shift problem caused by the difference in the reconstruction kernels (Section 2.2). Although FBPAug achieves comparable results on the target domain, our method outperforms it in terms of the average consistency score, 0.80 Dice Score against 0.76 \(\left(p_{1}<0.05,\ p_{2}<10^{-5}\right)\). The results are also in Table 3, row FBPAug.  
Then, we compare P-Consistency (which operates with the last layer of decoder) with F-Consistency and show it resulting in the significantly lower consistency score, 0.63 against 0.80 \(\left(p_{1}<0.05,\ p_{2}<10^{-10}\right)\). Thus, our findings align with the message [30] that the encoder layers contain more domain-specific information than the decoder ones. F-Consistency outperforms DANN. Although both methods score similar in target Dice Score, F-Consistency has an advantage over DANN in the consistency score: 0.80 against 0.78 \(\left(p_{1}<0.05,\ p_{2}<10^{-2}\right)\). Our intuition here is F-Consistency explicitly enforces features alignment, while DANN enforces features to be indistinguishable for the discriminator. The latter differently impact the consistency score, and F-Consistency performs better.  
We conclude the comparison of the methods with the qualitative analysis. In Figure 2, one could find examples of the Baseline, FBPAug, DANN, and F-Consistency predictions on the COVID-test dataset and compare them with the ground truth. Although all adaptation methods perform similar to the ground truth with minor inaccuracies, Baseline outputs the massive false positive predictions on the unseen domain. Additionally to the quantitative analysis above, the latter observation highlights the relevance of the domain adaptation problem in the COVID-19 segmentation task.  
In Figure 3, we visualize predictions of the same four methods on the paired images from the Paired-private dataset. For the Baseline, we observe an extreme inconsistency (Figure 3A) and massive false positive predictions in healthy lung tissues (Figure 3D) and even outside lungs (Figure 3B). For the adaptation methods, their predictions are visually more consistent inside every pair, which aligns with the consistency scores in Table 3. Despite the high consistency scores, FBPAug and DANN output more aggressive predictions. FBPAug predicts motion artifacts near the body regions (Figure 3A) and triggers similarly to the baseline on, most likely, healthy lung tissues (Figure 3B). DANN is more conservative but triggers on the consolidation-like tissues (Figure 3C,D). However, without the ground truth annotations on the paired data, we refer to this analysis as a discussion.  
Below, we investigate the generalization of the models trained with fewer data and the trade-off between consistency and segmentation quality.