## 8 Discussion  
In the first experiment, different vascular enhancement filters were compared. The task was to find out which of the five filters was the best, in terms of the segmentation quality measures. From the visualizations of the filters' enhancing effects in Figure 4, it can be seen that the RORPO filter performs very well. There is a minimum amount of image noise. In addition, there are no visible boundaries between the liver and the background that could affect the result. Although the image enhanced by the Frangi filter seems promising as well, the vessels are relatively thin. Visible boundaries and some noise are typical for the Meijering filter and the Sato filter. In terms of the segmentation quality, the dice scores for the original and the improved annotations prove that the RORPO is the most effective filter, and has permitted us to improve the dice score from 62.7% to 69.7% and to reduce the mean standard deviation from 13.8% to 4.8%. The improvement in dice score and reduction in standard deviation shows that the model can segment vessels more efficiently in different test patients. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Filters** & **Published** & **Dice score** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \hline  & \multicolumn{4}{c}{**Indirect comparison**} \\ \hline [PERSON] et al. [4] & 2018 & 67.3 & 96.4 & 73.7 & 97.4 \\ [PERSON] et al. [8] & 2018 & 75.3 & 97.6 & 76.7 & 98.8 \\ [PERSON] et al. [14] & 2021 & 75.46 & - & 76.9 & - \\ [PERSON] et al. [15] & 2021 & 71.6 & 98.5 & 75.4 & 99.5 \\ [PERSON] et al. [18] & 2020 & 68.7 & **99.8** & **78.6** & 99.2 \\ \hline \hline  & \multicolumn{4}{c}{**Direct comparison (re-implemented related works)**} \\ \hline [PERSON] et al. [16] & 2020 & 65.0 \(\pm\) 9.7 & **99.8 \(\pm\) 0.4** & 52.3 \(\pm\) 10.8 & 88.9 \(\pm\) 6.2 \\ [PERSON] et al. [23] & 2020 & 59.6 \(\pm\) 5.8 & 98.9 \(\pm\) 0.3 & 55.9 \(\pm\) 4.2 & 64.8 \(\pm\) 12.0 \\ \hline Our method & 2022 & **76.2 \(\pm\) 3.7** & 99.3 \(\pm\) 0.1 & 75.0 \(\pm\) 3.4 & **99.7 \(\pm\) 0.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method, with related works. The second experiment focused on leveraging the vessel enhancement effects of different filters in various segmentation pipelines. In FilerAdded, four enhancement filters were combined, and the model was trained on this combined data. In SegAdded, four models were trained with distinct enhancement filters, and the results were later combined when at least two models were able to predict the vessel pixel. In linear blending, the original image was combined with the RORPO enhanced image. By comparing the different pipelines, the linear blending provides the best results for all observed metrics except sensitivity. Here, the SegAdded approach performs better. Results were also obtained from different segmentation models (3D U-Net and 3D V-Net). In the third experiment, a detailed analysis of the proposed method was provided. We have explored the effect of RORPO on the original image. We have set the mixing ratio between RORPO and the original image, \(\alpha\), from 0% to 100%, with a step of 10%. The RORPO filter has a significant positive effect on the result. The best segmentation is achieved by using 40% of the original image and 60% of RORPO. The acceptable \(\alpha\) range is 40-60%. This proves that the role of background information is also helpful in segmenting the region of interest. The results are also performed on 3D U-Net and 3D V-Net performs better than 3D U-Net. This shows that for volumetric input, 3D V-Net performs better. Hence, we used V-Net in all further tests. However, 3D V-Net is more expensive in terms of computing resources, and the time for training is twice as long as that of 3D U-Net. Another evaluation of the proposed method focused on the use of different dice loss functions. The Weighted Dice Loss \(\beta_{l}=6\) outperformed other loss functions, achieving a dice score of 76.2% and a mean standard deviation of 3.7%.