## 3 Results
### Performance Analysis  
Table 3 illustrates the performance metrics obtained by comparing the automatic and manual delineations by averaging the results of the five validation folds during the fivefold cross-validation process. A DSC greater than 90% for ENet indicates excellent performances that justify the use of an automatic and independent operator method rather than the manual method, which, although more precise, is very time-consuming, as reported below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Sensitivity** & **PPV** & **DSC** & **VOE** & **VD** \\ \hline \multicolumn{6}{c}{ENet} \\ \hline Mean \(\pm\) SD & 91.54 \(\pm\) 7.49\% & 89.85 \(\pm\) 5.66\% & 90.52 \(\pm\) 5.49\% & 16.87 \(\pm\) 8.81\% & 2.11 \(\pm\) 8.53\% \\ \hline \(\pm\)CI (95\%) & 4.89\% & 3.70\% & 3.59\% & 5.76\% & 5.58\% \\ \hline \multicolumn{6}{c}{ERFNet} \\ \hline Mean \(\pm\) SD & 72.21 \(\pm\) 26.11\% & 87.00 \(\pm\) 8.00\% & 74.85 \(\pm\) 19.28\% & 36.85 \(\pm\) 21.87\% & \(-\)14.80 \(\pm\) 33.32\% \\ \hline \(\pm\)CI (95\%) & 17.06\% & 5.23\% & 12.60\% & 14.29\% & 21.77\% \\ \hline \end{tabular} ENet, Efficient Neural Network; ERFNet, efficient residual factorized ConvNet; PPV, positive predictive value; DSC, dice similarity coefficient; VOE, volume overlap error; VD, volumetric difference; SD, standard deviation; CI, confidence interval.  
\end{table}
Table 3: Performance results using ENet and ERFNet (fivefold cross-validation strategy).  
Figure 1: (a) Correlation graph, (b) Bland–Altman plot between manual and Efficient Neural Network (ENet) volumetric segmentation, (c) correlation graph, and (d) Bland–Altman plot between manual and Efficient Residual Factorized ConvNet (ERFNet) segmentation. RPC, reproducibility coefficient; SSE, sum of squared error; n, number of data points; \(p\)-value, Pearson correlation \(p\)-value; r\({}^{2}\), Pearson r-value squared; cc, cubic centimeters; KS, Kolmogorov–Smirnov test; SD, standard deviation.  
Figure 2 shows the training DSC and the loss function plots for one fold: a DSC > 90% was achieved in just ~20 iterations for ENet. ERFNet always turns out to be worse than ENet, except for the segmentation speed, as can be seen in Table 4.  
Specifically, using GPU hardware (NVIDIA QUADRO P4000 with 8 GB VRAM and 1792 CUDA Cores), ENet takes about 15 s for a whole segmentation versus 13 s of ERFNet. In the case of a CPU (Intel(R) Xeon(R) W-2125 CPU 4.00 GHz processor), ENet takes about 2 min versus 1 min of ERFNet. The manual approach required an average time of 3887 \(\pm\) 1600 s (approximately one hour per segmentation). Examples of the obtained segmentations are shown in Figure 3 for two patients with abdominal soft tissue sarcomas.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Number of Parameters** & **Size on Disk** & **Inference Times (s)/Sataset** &
\begin{tabular}{c} **Training Times (Days)/Dataset** \\ \end{tabular} \\ \hline  & Trainable & Non-Trainable & CPU & GPU & GPU \\ \hline ENet & 363,069 & 8354 & 5.8 MB & 113.10 & 15.64 & 5.31 \\ \hline ERFNet & 2,056,440 & 0 & 25.3 MB & 58.08 & 13.49 & 4.16 \\ \hline \hline \end{tabular}  
ENet, Efficient Neural Network; ERFNet, efficient residual factorized ConvNet; MB, megabyte; CPU, central processing unit; GPU, graphics processing unit; sec, seconds.  
\end{table}
Table 4: Comparison of computational complexity and performance between the two deep learning models.  
Figure 2: (a) Dice similarity coefficient and (b) loss function; Tversky loss plots for Efficient Neural Network (ENet) in red lines and Efficient Residual Factorized ConvNet (ERFNet) in blue lines during the training process for one fold.