## 4 Results  
### Network and Method Validation Results  
To compare FCNN networks, with the improvement in the proposed segmentation method by adding a cropping stage, the intermediate segmentation mask was also evaluated. Table 1 shows the segmentation results on the validation dataset using four FCNN after DLS1 with post-processing (Stage1), and after utilizing the whole pipeline proposed in Figure 5 (Full method).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Subject** & **Sensitivity** & **Precision** & **Dice** \\ \hline
3D U-net & Liver & 0.940 \(\pm\) 0.007 & 0.775 \(\pm\) 0.110 & 0.834 \(\pm\) 0.054 \\ (Stage1) & Tumors & 0 & 0 & 0 \\ \hline
3D U-net & Liver & 0.995 \(\pm\) 0.007 & 0.873 \(\pm\) 0.124 & 0.930 \(\pm\) 0.077 \\ (Full method) & Tumors & 0.167 \(\pm\) 0.421 & 0.051 \(\pm\) 0.412 & 0.093 \(\pm\) 0.365 \\ \hline V-net & Liver & 0.871 \(\pm\) 0.079 & 0.589 \(\pm\) 0.141 & 0.693 \(\pm\) 0.099 \\ (Stage1) & Tumors & 0 & 0 & 0 \\ \hline V-net & Liver & 0.975 \(\pm\) 0.026 & 0.762 \(\pm\) 0.147 & 0.848 \(\pm\) 0.099 \\ (Stage2) & Tumors & 0.3278 \(\pm\) 0.460 & 0.385 \(\pm\) 0.382 & 0.275 \(\pm\) 0.358 \\ \hline SegResNet & Liver & 0.916 \(\pm\) 0.062 & 0.746\(\mp\)0.102 & 0.815 \(\pm\) 0.046 \\ (Stage1) & Tumors & 0 & 0 & 0 \\ \hline SegResNet & Liver & 0.992 \(\pm\) 0.008 & 0.796 \(\pm\) 0.112 & 0.879 \(\pm\) 0.716 \\ (Stage2) & Tumors & 0.663 \(\pm\) 0.339 & 0.692 \(\pm\) 0.226 & 0.655 \(\pm\) 0.281 \\ \hline HighResNet & Liver & 0.994 \(\pm\) 0.003 & 0.859 \(\pm\) 0.044 & 0.919 \(\pm\) 0.026 \\ (Stage 1) & Tumors & 0.948 \(\pm\) 0.209 & 0.351 \(\pm\) 0.156 & 0.488 \(\pm\) 0.153 \\ \hline HighResNet & Liver & 0.988 \(\pm\) 0.008 & 0.896 \(\pm\) 0.035 & 0.942 \(\pm\) 0.017 \\ (Full method) & Tumors & 0.915 \(\pm\) 0.258 & 0.510 \(\pm\) 0.165 & 0.626 \(\pm\) 0.134 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results achieved on the validation set on the two stages with different FCNN networks.  
Figure 6: Designed workflow to utilize the deep learning-based tool for 3D segmentation for liver parenchyma and tumor segmentation to create a 3D model: (**a**) unannotated MRI volume; (**b**) segmentation inference using GUI; (**c**) medical expert corrections and verification; (**d**) 3D model rendering.  
From Table 1, an improvement in all metrics between Stage 1 and Stage 2 for all networks was observed. Only HighResNet was able to detect and segment the network from the full image, while three other networks detected tumors only using the full proposed method. Using only the one stage approach V-net achieved the lowest segmentation metrics for the liver segmentation with a Dice score of 0.693 \(\pm\) 0.099, while HighResNet achieved the highest value within all metrics with a Dice score of 0.919 \(\pm\) 0.026. After the second stage, all four networks were able to segment tumors from MRI volumes. SegResNet and HighResNet had similar performances in terms of Dice score for tumor segmentation, with higher tumor sensitivity for HighResNet of 0.915 \(\pm\) 0.258, and higher precision for SegResNet of 0.692 \(\pm\) 0.226. For the liver parenchyma segmentation, the best performance was achieved by HighResNet.  
Inference time with loading the model, pre-and post-processing for one volume varies between the networks used. For 3D U-net, it was 6.12 \(\pm\) 1.34 V-net--5.41 \(\pm\) 0.99, SegResNet--6.08 \(\pm\) 1.04 and for HighResNet it was 5.29 \(\pm\) 0.95 s per volume.