## 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
3D UNET + DS (last) & 73.7 & 80.0 & 77.7 \(\pm\) 1.6 & 78.0 \\ EXT 2 + DS (last) & 74.4 & 80.4 & 77.3 \(\pm\) 1.3 & 77.3 \\ EXT 2 + DS (best) & 71.0 & 80.2 & 76.7 \(\pm\) 1.7 & 76.8 \\ EXT 1 + DS (last) & 71.2 & 79.9 & 76.0 \(\pm\) 1.9 & 76.3 \\
3D UNET + DS (best) & 70.4 & 79.7 & 76.0 \(\pm\) 2.0 & 76.3 \\ EXT 1 + DS (best) & 64.5 & 80.5 & 75.3 \(\pm\) 3.0 & 75.9 \\
3D UNET (best) & 64.9 & 74.8 & 70.0 \(\pm\) 2.1 & 70.1 \\
3D UNET (last) & 61.1 & 74.4 & 69.0 \(\pm\) 2.7 & 69.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effectiveness of the proposed follicle detection methods trained and evaluated 50 times on the USOVA3D database. 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. For illustration, we also calculated the effectiveness of better follicle detection methods at the level of all detected voxels, i.e., we did not consider to which follicle the voxel belonged. Actually, we evaluated the effectiveness of a binary segmentation of selected 3D detection methods (segmented voxel value 1 determines the Region of Interest, while value 0 means the background). Besides the classical metrics written above, we also calculated the Accuracy (ACC) metric. We were not able to calculate the Accuracy at the 'follicle level', as our CNN networks do not return information about True Negatives. It should also be noted that the Dice Similarity Coefficient and the F1 score are the same in the case of Boolean or binary data analysis. The calculated metrics are collected in Table 4.  
Our methods were then evaluated in respect to the effectiveness of ovary detection. If the 3D U-Net (with or without Deep Supervision) was trained in detecting ovaries directly (as by follicle detection), such an approach was significantly less successful than the baseline methods. A similar result was observed if our U-Net extensions were trained to detect follicles and ovaries simultaneously. Some results of these non-successful experiments are not reported. To detect the ovaries, we therefore utilised variations of both the U-Net extensions (i.e., EXT 1 and EXT 2) proposed in Section 3.3, whereas the network from the first stage was pretrained on the problem of ovarian follicle detection. A kind of Transfer Learning (see Section 3.5) was employed, because the weights of the network from the first stage were frozen, and were no longer adapted during the ovary detection training. Table 5 contains the results of ovary detection for both the proposed pretrained U-Net extensions (EXT 1 and EXT 2), with or without Deep Supervision (+DS). Each method was trained and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **median(\(\xi_{vol}\))** & **min(\(\xi_{vol}\))** & **max(\(\xi_{vol}\))** & \(\xi_{alg}\) \\ \hline Rater 1 vs. rater 2 & 84.1 & 72.7 & 91.2 & 83.9 \\ Rater 2 vs. rater 1 & 84.5 & 70.4 & 91.5 & 83.1 \\ \hline EXT 1 + DS (best) & 80.9 & 63.2 & 93.5 & 80.5 \\ EXT 2 + DS (last) & 82.8 & 65.3 & 92.5 & 80.4 \\ EXT 2 + DS (best) & 81.9 & 63.4 & 92.4 & 80.2 \\
3D UNET + DS (last) & 83.0 & 67.5 & 93.3 & 80.0 \\ EXT 1 + DS (last) & 80.7 & 65.9 & 93.2 & 79.9 \\
3D UNET + DS (best) & 82.6 & 56.1 & 93.9 & 79.7 \\
3D DWT (baseline 1) & 79.3 & 59.7 & 90.6 & 78.2 \\
3D UNET (best) & 76.0 & 59.6 & 90.4 & 74.8 \\
3D UNET (last) & 76.0 & 54.0 & 93.0 & 74.4 \\ SNET (best) & 74.6 & 47.7 & 89.1 & 72.6 \\
2D UNET (baseline 2) & 75.1 & 43.8 & 91.5 & 72.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effectiveness of the proposed follicle detection methods compared to the state-of-the-art and inter-rater variability. Presented are the final score statistics and the overall algorithm’s score on the USOVA3D database. Entries are sorted with respect to the overall score.