## 5 Results
### U-Net Segmentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threshold** & **Dice Co-Eff** & **MR \({}^{a}\)** & **MR Aug \({}^{b}\)** & **MR with CT \({}^{c}\)** & **MR with CT Aug \({}^{d}\)** \\ \hline \multirow{2}{*}{0.36} & max & 0.9520 & 0.9463 & 0.9506 & 0.9537 \\  & mean & 0.8365 & 0.8168 & 0.8315 & 0.8069 \\ \hline \multirow{2}{*}{0.38} & max & 0.9529 & 0.9456 & 0.9505 & 0.9552 \\  & mean & 0.8365 & 0.8164 & 0.8339 & 0.8076 \\ \hline \multirow{2}{*}{0.40} & max & 0.9539 & 0.9472 & 0.9496 & 0.9570 \\  & mean & 0.8366 & 0.8160 & 0.8361 & 0.8083 \\ \hline \multirow{2}{*}{0.42} & max & 0.9553 & 0.9490 & 0.9488 & 0.9586 \\  & mean & 0.8365 & 0.8150 & 0.8380 & 0.8088 \\ \hline \multirow{2}{*}{0.44} & max & 0.9564 & 0.9496 & 0.9489 & 0.9595 \\  & mean & 0.8364 & 0.8137 & 0.8398 & 0.8094 \\ \hline \multirow{2}{*}{0.46} & max & 0.9579 & 0.9514 & 0.9483 & 0.9615 \\  & mean & 0.8361 & 0.8122 & 0.8416 & 0.8099 \\ \hline \multirow{2}{*}{0.48} & max & 0.9585 & 0.9535 & 0.9481 & 0.9625 \\  & mean & 0.8358 & 0.8101 & 0.8430 & 0.8104 \\ \hline \multirow{2}{*}{0.50} & max & 0.9600 & 0.9555 & 0.9508 & 0.9629 \\  & mean & 0.8354 & 0.8080 & 0.8444 & 0.8105 \\ \hline \multirow{2}{*}{0.52} & max & 0.9613 & 0.9576 & 0.9537 & 0.9638 \\  & mean & 0.8350 & 0.8057 & 0.8456 & 0.8104 \\ \hline \multirow{2}{*}{0.54} & max & 0.9627 & 0.9594 & 0.9553 & 0.9656 \\  & mean & 0.8343 & 0.8034 & 0.8465 & 0.8102 \\ \hline \multirow{2}{*}{0.56} & max & 0.9642 & 0.9603 & 0.9579 & 0.9665 \\  & mean & 0.8336 & 0.8010 & 0.8471 & 0.8097 \\ \hline \multirow{2}{*}{0.58} & max & 0.9658 & 0.9609 & 0.9599 & 0.9677 \\  & mean & 0.8328 & 0.7983 & 0.8475 & 0.8092 \\ \hline \multirow{2}{*}{0.60} & max & 0.9674 & 0.9630 & 0.9621 & 0.9685 \\  & mean & 0.8318 & 0.7953 & 0.8478 & 0.8085 \\ \hline \end{tabular} \(a\)–Magnetic Resonance Imaging dataset, \(b\)–Magnetic Resonance Imaging Augmented dataset, \(c\)—Magnetic Resonance Imaging and Computed Tomography mixed datasets, \(d\)—Magnetic Resonance Imaging and Computed Tomography Augmented mixed datasets.  
\end{table} 
Table 1: The values of the ROC-AUC per epoch for each experiment. Best values are indicated in a blue color. Figure 8: The values of the ROC-AUC per epoch for each experiment. number of false-positive values. Figure 9 presents a visualization of the results achieved by the best model (MR with CT). Figure 10 presents a comparison of the Dice coefficients for two datasets--raw MR and MRI with augmented slices. Increasing the training dataset by adding augmented slices has an impact on obtaining a higher Dice coefficient, which amounts to better results. With raw MRI data, there are more results with a low Dice coefficient and visible outer layers (Dice coefficients from 0.0 to 0.2). With an augmented dataset, the lowest Dice coefficient is above 0.2, which means reducing the outliers. Figure 11 presents a comparison of the Dice coefficients for two datasets--raw MRI and MRI mixed with CT slices. Mixing the dataset increases the minimum Dice coefficient by almost double and on average produces better results. Figure 10: Distribution of Dice coefficients for two experiments: MR without augmentation (raw) and MR with augmented slices. Figure 9: Prostate segmentation results: (**a**) prostate detection image post-processing that resulted from YOLOv4 and is a U-Net input, the (**b**) prediction mask, and the (**c**) prediction result.