## 3 Results
### Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Dataset** & **N** & \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 \multirow{6}{*}{MR} & Girona & 5 & 0.8980 & 0.9113 & 0.8467 & 3.7240 & 3.7873 & 4.2054 & 1.3305 & 1.3187 & 2.0323 \\  & Promise12 & 7 & 0.9148 & 0.9118 & 0.8919 & 4.2876 & 3.6000 & 7.2000 & 1.0135 & 0.9680 & 1.2757 \\  & Prostate-3T & 2 & 0.9222 & 0.9222 & 0.9099 & 3.6000 & 3.6000 & 3.6000 & 0.9190 & 0.9190 & 1.0719 \\  & IVO & 42 & 0.9136 & 0.9182 & 0.8094 & 3.9947 & 4.0002 & 6.9347 & 0.9569 & 0.9013 & 2.0356 \\  & Private & 13 & 0.9251 & 0.9228 & 0.8993 & 3.3363 & 3.1736 & 4.5122 & 0.9190 & 0.8525 & 1.2815 \\ \cline{2-10}  & All & 69 & 0.9150 & 0.9179 & 0.8094 & 3.8693 & 3.9995 & 7.2000 & 0.9815 & 0.9311 & 2.0356 \\ \hline \multirow{6}{*}{US} & IVO & 24 & 0.9215 & 0.9256 & 0.8456 & 3.4295 & 3.1210 & 9.9997 & 1.1825 & 1.0539 & 2.8573 \\  & Private & 12 & 0.9131 & 0.9133 & 0.8960 & 3.6317 & 3.7025 & 6.1216 & 1.1872 & 1.2008 & 1.7809 \\ \cline{1-1} \cline{2-10}  & All & 36 & 0.9187 & 0.9235 & 0.8456 & 3.4969 & 3.2863 & 9.9997 & 1.1840 & 1.1102 & 2.8573 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results for all datasets and models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset (MR)**} & \multirow{2}{*}{**N**} & \multicolumn{3}{c}{**Mean \(DSC\)**} \\ \cline{3-5}  & & **Predicted** & **Predicted** & **GT** \\  & & **\& GT** & **\& GT2** \\ \hline Girona & 5 & 0.8980 & 0.9057 & 0.8657 \\ Promise12 & 7 & 0.9148 & 0.9032 & 0.8825 \\ Prostate-3T & 2 & 0.9222 & 0.8995 & 0.9026 \\ \hline All above & 14 & 0.9099 & 0.9035 & 0.8794 \\ \hline \multicolumn{5}{c}{_p_-value against last column} \\ \cline{3-5}  & & 0.0035 & 0.0258 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of the predictions against GT and GT2, as well as GT against GT2 (inter-expert performance). achieved an excellent 0.941 mean \(DSC\) by applying a 2D U-Net on radially sampled slices of the 3D-US and then reconstructing the full 3D volume. As an example on the problem of 2D-US segmentation, ref. [35] achieved a mean \(DSC\) of 93.9 by using an ensemble of five CNNs. This last result, however, is not directly comparable, as in 2D-US segmentation the \(DSC\) is evaluated on a per-slice basis, instead of the prostate as a whole. Promise12 is an ongoing prostate segmentation challenge, wherein 50 MR prostate images are provided along with their segmentation masks (dataset \"Promise12\"), and 30 additional images are provided without segmentations as a test set (dataset \"Promise12_test\"). Table 4 shows the performance of the model on \"Promise12_test\" along with the five best entries to the Promise12 challenge. For this specific dataset, the predicted segmentation masks are uploaded and evaluated in the servers of the challenge, and the results are publicly posted online thereafter [36]. As it should be expected, the mean and median for our model are similar to the results obtained for the other test sets (Table 2). Also, comparing it to the other entries (Table 4), our model achieves very similar results for all metrics; yet, its Challenge Score falls behind. That said, this is also to be expected since, unlike the other contestants, no fine-tuning was performed to improve the results for this dataset in particular. BOWDA-net [23], for example, uses an adversarial domain adaptation strategy to transform the images from a second training dataset to the domain of the \"Promise12\" dataset, therefore improving the performance only on \"Promise12_test\". Lastly, our model used just 41 out of the 50 images provided in the \"Promise12\" dataset for training, as two were discarded and seven were used for testing.