## 3 Results
### Ablation Studies  
Table 5 contains the results of the ablation studies, which were performed by changing one single aspect of the baseline MR model at a time. Wilcoxon tests were performed against the baseline to check for significance (\(p\)-value \(<0.05\)). Also, a single experiment was performed on the US model by retraining it without the use of transfer learning.  
Firstly, the two post-processing techniques discussed in this paper (Checkpoint Ensembling and Neural Resolution Enhancement) are analyzed. Both show high statistical significance (\(p\)-values \(<0.01\)) in terms of \(DSC\) and ABD. In fact, out of all the experiments conducted in this Section, only these two were found to make a statistically significant difference, probably since the worsening of the metrics, even if minor, is sustained for all images. These post-processing methods affect in no way the training process or the model, as they are applied at a later stage; therefore they are a simple and free bonus in performance, only at the cost of increased inference time.  
Secondly, a battery of tests involving architectural changes (which require retraining) is presented. Even if none of these experiments showed statistical significance, several conclusions can still be extracted cautiously.  
The first two experiments are an attempt to lower the complexity of the baseline model by either reducing the number of resolution levels of the network, or the amount of layers (this is: \"standard\" convolutions) per level. In both cases, even if the differences with respect to the baseline were small, a decrease in performance can be observed for the majority of the metrics, which justifies the use of the more complex baseline architecture if possible.  
Figure 7: Worst HD95 (7.2 mm) of all MR test datasets. Two slices from the apex (**left** and **center**) are missed by the algorithm, hence amounting to a minimum of \(2\times 3\) mm of error, plus some extra mm from the segmentation errors commited in the third slice (**right**).  
In the next two experiments, models based exclusively on the resnet architecture (with residual connections applied every four consecutive convolutions) were employed. Despite having as much as four times the amount of parameters as compared to the baseline, these models were the worst performing out of all analyzed in this Section, hence showing the power of the densenet architecture.  
The next test consisted in replacing the PReLU activations with ReLU activations. Despite this having a very small influence in performance, the metrics are overall better in the baseline, and PReLU is therefore preferred given its negligible impact on model complexity.  
For the following test, Deep Supervision was deactivated. In general, most of the metrics show a small improvement with this architectural modification. In our internal validation tests, this technique seemed to provide a small boost in performance and, as such, it was added to the final model. Furthermore, it stabilized the initial steps of the training procedure. However, in light of these results, its usefulness remains now in question.  
For the last test, the US segmentation model was retrained using random weight initialization, instead of using the weights from the MR model for transfer learning. Although the fine-tuned model converged faster, the results suggest that training from scratch might be preferable in situations like this one, where the amount of training data is sufficient. In any event, the generality of the architecture and pre-/post-processing methodology still holds, even if the weights of the MR prostate segmentation model are not particularly useful for the problem of US prostate segmentation.