## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **min(\(\xi_{alg}\))** & **max(\(\xi_{alg}\))** & **mean(\(\xi_{alg}\)) \(\pm\) std(\(\xi_{alg}\))** & **median(\(\xi_{alg}\))** \\ \hline EXT 2 + DS (last) & 63.0 & 76.0 & 71.7 \(\pm\) 2.5 & 71.8 \\ EXT 1 + DS (best) & 62.1 & 76.2 & 70.3 \(\pm\) 3.3 & 70.5 \\ EXT 2 + DS (best) & 60.0 & 74.9 & 69.1 \(\pm\) 3.7 & 69.4 \\ EXT 1 + DS (last) & 53.1 & 77.7 & 68.9 \(\pm\) 5.0 & 68.6 \\ EXT 1 (best) & 59.1 & 72.6 & 66.9 \(\pm\) 3.1 & 67.0 \\ EXT 1 (last) & 53.7 & 71.8 & 64.7 \(\pm\) 4.1 & 65.0 \\
3D UNET (best) & 56.8 & 72.8 & 64.2 \(\pm\) 3.8 & 64.6 \\
3D UNET + DS (best) & 56.1 & 70.8 & 63.3 \(\pm\) 3.4 & 63.3 \\ EXT 2 (best) & 54.0 & 68.9 & 62.2 \(\pm\) 3.5 & 62.3 \\ EXT 2 (last) & 54.1 & 68.7 & 62.3 \(\pm\) 3.3 & 62.3 \\
3D UNET + DS (last) & 50.0 & 63.0 & 58.0 \(\pm\) 2.8 & 58.4 \\
3D UNET (last) & 43.8 & 69.8 & 56.8 \(\pm\) 5.0 & 56.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effectiveness of the proposed ovary detection methods trained and evaluated 50 times on the USOVA3D database. The minimum, maximum, mean and median of the overall algorithm’s score are presented over 50 runs. Entries are sorted with respect to the median value. Similarly as for follicles, we also calculated the effectiveness of better ovary detection methods at the level of all detected voxels, i.e., all metrics were calculated across all properly segmented voxels and not at the level of the entire ovary. The results are gathered in Table 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **median(\(\mathbf{\xi}_{pol}\))** & **min(\(\mathbf{\xi}_{pol}\))** & **max(\(\mathbf{\xi}_{pol}\))** & \(\mathbf{\xi}_{alg}\) \\ \hline Rater 1 vs. rater 2 & 79.1 & 52.7 & 96.0 & 76.1 \\ Rater 2 vs. rater 1 & 78.8 & 45.5 & 96.1 & 75.5 \\ \hline EXT 1 + DS (last) & 79.9 & 51.6 & 91.5 & 77.7 \\ EXT 1 + DS (best) & 78.8 & 50.3 & 92.6 & 76.2 \\ EXT 2 + DS (last) & 80.9 & 52.1 & 93.4 & 76.0 \\ EXT 2 + DS (best) & 81.6 & 47.7 & 92.0 & 74.9 \\
3D UNET (best) & 75.8 & 47.7 & 88.4 & 72.8 \\ EXT 1 (best) & 75.9 & 48.9 & 93.3 & 72.6 \\
2D UNET (baseline 2) & 73.6 & 40.5 & 87.9 & 72.2 \\ EXT 1 (last) & 74.9 & 48.8 & 92.3 & 71.8 \\
3D UNET + DS (best) & 73.7 & 47.7 & 91.1 & 70.8 \\
3D UNET (last) & 71.4 & 42.5 & 92.2 & 69.8 \\ SNET (best) & 65.7 & 45.6 & 86.7 & 69.4 \\ EXT 2 (best) & 69.6 & 50.9 & 88.3 & 68.9 \\ EXT 2 (last) & 65.7 & 47.5 & 89.6 & 68.7 \\
3D DWT (baseline 1) & 72.5 & 18.3 & 87.1 & 63.3 \\
3D UNET + DS (last) & 59.3 & 42.2 & 92.6 & 63.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effectiveness of the proposed ovary detection methods compared to the state-of-the-art and inter-rater variability. The final score statistics are presented, together with the overall algorithm’s score on the USOVA3D database. Entries are sorted with respect to the overall score. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **DSC** & **JCI** \\ \hline Rater 1 vs. rater 2 & 0.880 \(\pm\) 0.070 & 0.793 \(\pm\) 0.111 \\ Rater 2 vs. rater 1 & 0.880 \(\pm\) 0.070 & 0.793 \(\pm\) 0.111 \\ \hline EXT 1 + DS (last) & 0.865 \(\pm\) 0.088 & 0.771 \(\pm\) 0.127 \\ EXT 2 + DS (last) & 0.852 \(\pm\) 0.102 & 0.753 \(\pm\) 0.143 \\
3D UNET (best) & 0.833 \(\pm\) 0.112 & 0.728 \(\pm\) 0.152 \\
3D UNET + DS (best) & 0.829 \(\pm\) 0.108 & 0.722 \(\pm\) 0.147 \\ SNET (best) & 0.802 \(\pm\) 0.131 & 0.686 \(\pm\) 0.162 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effectiveness of the proposed ovary detection methods compared to the state-of-the-art and inter-rater variability. Presented are the mean and Standard Deviation of the Dice Similarity Coefficient (DSC) and Jaccard Index (JCI) on the USOVA3D database.