## 5 Discussion
### Individual Datasets  
In the first NIDDK dataset, the T2-weighted MR scans had a slice thickness of 9 mm, three times that of the T1-weighted MR scans. The T2-weighted slice in Figure 3 is therefore interpolated from multiple slices. The multistage network therefore not only performs registration of the T2-weighted scans on the corresponding T1-weighted scans, but also reconstructs thinner (3 mm) T2-weighted slices using T1-weighted MR scans. We applied the networks trained on the first NIDDK dataset to the second NIDDK dataset, without finetuning the networks with the second dataset. This yielded registration results with a high spatial alignment, which indicates that generalization to new data was successful. In the external kidney dataset, kidney segmentations were created for the T1-weighted scans, whereas kidney cyst segmentations were created for the T2-weighted MR scans. A Dice coefficient of 100% cannot be achieved even with perfect registration and alignment of the kidneys as the two types of segmentation focus on different aspects. Kidney segmentations cover the entire kidney, including cysts and the renal parenchyma, whereas kidney cyst segmentations exclusively mark the cysts themselves. Moreover, a significant increase in the Dice coefficient does not necessarily indicate better spatial alignment. Hence, a visual evaluation of the registration results is important. We also applied the networks trained on the first NIDDK dataset to the external kidney dataset. Good registration results were achieved without needing to finetune the networks. Compared to this, the multistage network yielded registration results with a higher Dice coefficient on the augmented external kidney dataset (last row in Table 6). This is due to the fact that the multistage network trained on the first NIDDK dataset was applied to the external kidney dataset without finetuning, whereas the multistage network was finetuned on the augmented external kidney dataset before evaluation on the augmented external kidney dataset. Without finetuning, the multistage network performed poorly on the augmented dataset. The reason for this is that the augmentation generated transformations, such as rotation or translation, which do not occur to this extent in the training dataset (first NIDDK dataset). Hence, finetuning of the multistage network with the augmented external kidney dataset was necessary in order to be able to correct these deformations as well. We augmented the external kidney dataset, i.e., performed artificial transformations similar to those used to augment training datasets, i.e., rotation, translation, scaling and deformation. In further work, the deformation of the abdomen due to respiration could also be simulated in order to investigate to what extent this deformation can be corrected with the proposed multistage network. For jointly finetuning all subnetworks of the benchmark and multistage network with the augmented external kidney dataset, we used a large value for the lambda parameter (10) to regularize the deformation field. If the networks were trained with smaller values for the lambda parameter, the networks produced deformations of the moving image that were not medically plausible. Using a large value for the lambda parameter then resulted in a very low level of image folding, which leads to a medically plausible transformation. Similarly, for the M\({}^{2}\)OLIE liver dataset, a large lambda parameter (4.5) was chosen to regularize the deformation field. However, medically implausible deformations were visible in the moved image at one area. These can be prevented by increasing the lambda parameter when finetuning the benchmark and proposed multistage network. However, this then leads to a worse registration result, i.e., lower overlap of all corresponding structures, compared to finetuning with a lambda parameter of 4.5.