## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **S** & **P** & **DSC** & **JCI** & **F1** \\ \hline Rater 1 vs. rater 2 & 0.881 \(\pm\) 0.152 & 0.850 \(\pm\) 0.254 & 0.863 \(\pm\) 0.055 & 0.769 \(\pm\) 0.076 & 0.825 \(\pm\) 0.168 \\ Rater 2 vs. rater 1 & 0.850 \(\pm\) 0.254 & 0.881 \(\pm\) 0.152 & 0.863 \(\pm\) 0.055 & 0.769 \(\pm\) 0.076 & 0.825 \(\pm\) 0.168 \\ \hline EXT 1 + DS (best) & 0.795 \(\pm\) 0.245 & 0.940 \(\pm\) 0.141 & 0.812 \(\pm\) 0.109 & 0.715 \(\pm\) 0.133 & 0.830 \(\pm\) 0.190 \\ EXT 2 + DS (last) & 0.799 \(\pm\) 0.253 & 0.943 \(\pm\) 0.147 & 0.808 \(\pm\) 0.113 & 0.710 \(\pm\) 0.131 & 0.828 \(\pm\) 0.192 \\
3D UNET + DS & 0.768 \(\pm\) 0.251 & 0.970 \(\pm\) 0.097 & 0.811 \(\pm\) 0.102 & 0.712 \(\pm\) 0.125 & 0.829 \(\pm\) 0.189 \\ (last) & & & & & \\
3D UNET (best) & 0.758 \(\pm\) 0.256 & 0.852 \(\pm\) 0.215 & 0.791 \(\pm\) 0.095 & 0.682 \(\pm\) 0.117 & 0.760 \(\pm\) 0.193 \\ SNET (best) & 0.797 \(\pm\) 0.248 & 0.900 \(\pm\) 0.174 & 0.719 \(\pm\) 0.153 & 0.613 \(\pm\) 0.171 & 0.808 \(\pm\) 0.182 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effectiveness of the proposed follicle detection methods compared to the state-of-the-art and inter-rater variability. Presented are the mean and Standard Deviation of Sensitivity or Recall (S), Precision (P), Dice Similarity Coefficient (DSC), Jaccard Index (JCI), and the F1 score (F1) on the USOVA3D database. evaluated 50 times on the USOVA3D database. Minimum, maximum, mean, and median are presented of the algorithm's effectiveness over 50 runs. The 'best' designates that the CNN model with the lowest validation loss was selected, while the 'last' means that the model was picked in a particular run after the final training step. In a similar way to the follicles, a comparison was also made with the state-of-the-art method SNET [11], USOVA3D baseline functions, and with the variability of both raters in ovary annotating. The results are gathered in Table 6. The more successful variants of the detection methods from this table were also evaluated using the Dice Similarity Coefficient and Jaccard Index (Sensitivity, Precision, and F1 score equals 1 for all ovary detection methods!). The mean and Standard Deviation of both metrics are presented in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **S** & **P** & **DSC** & **JCI** & **ACC** \\ \hline Rater 1 vs. rater 2 & 0.904 \(\pm\) 0.056 & 0.905 \(\pm\) 0.067 & 0.902 \(\pm\) 0.039 & 0.824 \(\pm\) 0.063 & 0.977 \(\pm\) 0.013 \\ Rater 2 vs. rater 1 & 0.905 \(\pm\) 0.067 & 0.904 \(\pm\) 0.056 & 0.902 \(\pm\) 0.039 & 0.824 \(\pm\) 0.063 & 0.977 \(\pm\) 0.013 \\ \hline EXT 1 + DS (best) & 0.809 \(\pm\) 0.135 & 0.958 \(\pm\) 0.039 & 0.871 \(\pm\) 0.089 & 0.781 \(\pm\) 0.128 & 0.974 \(\pm\) 0.017 \\ EXT 2 + DS (last) & 0.806 \(\pm\) 0.143 & 0.956 \(\pm\) 0.044 & 0.866 \(\pm\) 0.092 & 0.775 \(\pm\) 0.131 & 0.973 \(\pm\) 0.017 \\
3D UNET + DS & & & & & \\ (last) & 0.794 \(\pm\) 0.135 & 0.960 \(\pm\) 0.041 & 0.860 \(\pm\) 0.086 & 0.767 \(\pm\) 0.124 & 0.971 \(\pm\) 0.020 \\
3D UNET (best) & 0.800 \(\pm\) 0.140 & 0.919 \(\pm\) 0.082 & 0.849 \(\pm\) 0.104 & 0.750 \(\pm\) 0.141 & 0.969 \(\pm\) 0.020 \\ SNET (best) & 0.757 \(\pm\) 0.206 & 0.939 \(\pm\) 0.050 & 0.821 \(\pm\) 0.154 & 0.720 \(\pm\) 0.193 & 0.969 \(\pm\) 0.021 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effectiveness of the proposed follicle detection methods compared to the state-of-the-art and inter-rater variability, calculated on the ‘voxel level’, where it is not considered to which follicle the voxel belongs. Presented are the mean and Standard Deviation of Sensitivity or Recall (S), Precision (P), Dice Similarity Coefficient (DSC), Jaccard Index (JCI), and the Accuracy (ACC) on the USOVA3D database.