## 4 Numerical Results
The results were obtained after using a complete system with U-Net support. Green is the starting search area while red is the automatic kidney system. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sm without U-Net** & **Sm U-Net Supported** & \\ \hline
**Group No** & **Mean F1-Score \(\pm\) SD (95\% CI Range)** & **Mean F1-Score \(\pm\) SD (95\% CI Range)** & \(p\)**-Value** \\ \hline
1 & \(80.18\pm 12.02\) (76.83–83.52) & \(89.06\pm 8.93\) (86.16–91.95) & \(p<0.0005\) \\
2 & \(79.16\pm 13.32\) (75.59–82.73) & \(86.34\pm 9.93\) (83.25–89.44) & \(p<0.0005\) \\
3 & \(79.37\pm 13.35\) (75.76–82.98) & \(86.55\pm 8.64\) (83.89–89.21) & \(p<0.0006\) \\
4 & \(78.49\pm 13.25\) (74.51–82.48) & \(84.95\pm 11.39\) (81.68–88.22) & \(p<0.0003\) \\
5 & \(82.49\pm 13.99\) (78.34–86.65) & \(88.40\pm 9.78\) (85.76–91.04) & \(p<0.0004\) \\
6 & \(85.66\pm 8.82\) (83.28–88.05) & \(88.26\pm 10.44\) (85.46–91.06) & \(p<0.0004\) \\
7 & \(88.32\pm 6.78\) (86.69–89.95) & \(90.39\pm 11.68\) (85.94–94.83) & \(p<0.0001\) \\
8 & \(81.90\pm 13.45\) (78.62–85.18) & \(89.20\pm 10.46\) (85.05–93.35) & \(p<0.0004\) \\
9 & \(80.85\pm 11.64\) (76.36–85.34) & \(91.34\pm 8.56\) (89.07–93.61) & \(p<0.0004\) \\
10 & \(87.93\pm 6.46\) (86.13–89.73) & \(88.60\pm 11.00\) (85.65–91.54) & \(p<0.0001\) \\
11 & \(85.04\pm 11.14\) (82.11–87.97) & \(90.68\pm 8.95\) (88.37–93.00) & \(p<0.0003\) \\
12 & \(86.68\pm 7.98\) (84.52–88.84) & \(90.73\pm 9.02\) (88.38–93.09) & \(p<0.0005\) \\
13 & \(85.37\pm 9.32\) (82.83–87.92) & \(91.18\pm 9.02\) (87.77–94.59) & \(p<0.0003\) \\
14 & \(87.44\pm 7.42\) (84.97–89.92) & \(90.76\pm 9.13\) (88.34–93.18) & \(p<0.0004\) \\
15 & \(88.30\pm 7.00\) (86.56–90.03) & \(91.75\pm 7.00\) (89.95–93.56) & \(p<0.0005\) \\
16 & \(89.06\pm 6.74\) (87.47–90.64) & \(90.62\pm 6.86\) (88.85–92.39) & \(p<0.0005\) \\ \hline average & \(84.14\pm 10.17\) (81.29–87.00) & \(89.30\pm 9.42\) (86.47–92.13) & \(p<0.0004\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table showing the results of automatic kidney detection in the Ct images. The results were obtained by cross-validation calculating the F1-score for the system without U-Net and with the support of the U-Net network. Standard deviation (SD), confidence interval (CI) and \(p\)-value were calculated for each group. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **U-Net Threshold 20** & **U-Net Threshold 35** & **U-Net Threshold 50** & **U-Net Threshold Using** \\ \hline
**F1-Score** & **F1-Score** & **F1-Score** & **F1-Score** \\ \hline average & 59.57 & 79.45 & 65.34 & 62.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Table showing the results of kidney detection in the CT images. The results were obtained by calculating the F1-score measure for the U-Net system. The threshold value T equals 20, 35, and 50 was used to obtain the results. On right column is threshold using Otsu’s method. The analysis of both cases shows that the system is characterized by high accuracy of diagnosis both for each part of the abdominal section. Additionally, we performed the performance analysis of the developed technique of automatic kidney detection. The elapsed time measured from loading the full set of slices for one patient to the generation of the binary kidney mask of all slices was examined. Each data set consists of 20 and 50 raw CT images. We conducted a performance analysis for 10 randomly selected cases. Two approaches were tested:  
* System based on extended maxima transformations, described in Section 3.4. * System based on U-NET raw prediction. Tests were performed on a computer with Intel (R) Core (TM) i7-7820 HQ, 2.90 GHz, 4 Cores, 16 GB installed physical memory and graphics card: NVIDIA Quadro M1200. Table 4 shows the detailed test results of performance tests. Figure 8: The image shows a visualization of selected slices. Green is the starting search area while red is the automatic kidney system. Figure 9: The image shows a visualization of selected slices. Green is the starting search area while red is the automatic kidney system.