## 3 Results
### Qualitative Results  
To asses these results qualitatively, in Figure 6a-n, the center 100 mm \(\times\) 100 mm crop (85 mm \(\times\) 85 mm in the case of US images) of three slices from the worst and best performing images (in terms of \(DSC\)) from each dataset have been represented, along with the GT (in red), the GT2 (in blue, when available) and the predicted segmentations (in green). Figures were generated using Python library plot_lib [38].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Challenge Score**} & \multirow{2}{*}{**Name**} & \multicolumn{3}{c}{\(DSC\)} & \multicolumn{3}{c}{**HD95 (mm)**} & \multicolumn{3}{c}{**ABD (mm)**} \\ \cline{3-10}  & & & **Mean** & **Median** & **Min.** & **Mean** & **Median** & **Max.** & **Mean** & **Median** & **Max.** \\ \hline
91.9072 & MSD-Net & 0.9299 & 0.9323 & 0.8890 & 3.5512 & 3.3454 & 7.3344 & 1.1160 & 1.0968 & 1.6777 \\
90.7993 & Edge Att. & 0.9118 & 0.9136 & 0.8672 & 4.3095 & 3.9362 & 7.6217 & 1.4264 & 1.4004 & 2.3584 \\
90.3441 & HD\_Net & 0.9135 & 0.9129 & 0.8398 & 3.9331 & 3.7134 & 5.9674 & 1.3614 & 1.3090 & 2.2662 \\
89.6507 & nnU-Net & 0.9194 & 0.9272 & 0.8406 & 3.9509 & 3.7276 & 6.8301 & 1.2431 & 1.1771 & 2.1693 \\
89.5858 & Bowda-Net & 0.9141 & 0.9222 & 0.8367 & 4.2654 & 3.8969 & 7.7235 & 1.3451 & 1.2763 & 2.2920 \\ \hline
88.5397 & Ours & 0.9137 & 0.9168 & 0.8741 & 4.1176 & 3.8449 & 7.8605 & 1.3197 & 1.2864 & 1.8129 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MR model performance on “Promise12_test” along with the five best entries as of December 2020.  
Regarding the worst cases, despite being the poorest performers, the differences are relatively small and often the model proposal is arguably superior to the GT. Furthermore, the central slices are almost identical in all instances, and it is only towards base and apex  
Figure 6: Worst (**left**) and best (**right**) segmentations in terms of \(DSC\) for each dataset (green: model, red: GT, blue: GT2).  
where the differences emerge. One of such discrepancies is the point at which the apex and the base begin, which oftentimes depends on the segmentation criteria, as it can be seen, for instance, in Figure 5(a), where the CNN indicates the presence of prostate in the rightmost slice (at the base), while the GT label does not (although GT2 does). Finally, at several ambiguous instances (such as in the middle slice of Figure 5(a) and the rightmost slice of Figures 5(b),j), the predicted mask (in green) behaves as an average between both experts. As discussed, this is a very desirable property for the model to have, and this is what allows it to outperform single experts on their own (as demonstrated in Table 3).  
As for the best cases, it can be seen that they are mostly represented by larger prostates, as they are comparatively easier to segment, and also the \(DSC\) metric is biased towards them. As a curiosity, the rightmost slice in Figure 5(n) shows how the model has learned to avoid segmenting the catheter balloon that is used in prostate biopsies, the procedure during which the US images were acquired.  
In terms of HD95, the worst MR case, which corresponds to an HD95 of 7.2 mm, is shown in Figure 7. As it can be seen, two slices from the apex are missed by the algorithm, hence amounting to a minimum of \(2\times 3\) mm of error, plus some extra mm. The worst performing MR case in terms of ABD coincides with the worst performing prostate in terms of \(DSC\), which can be found in Figure 5(d).