## 8 Discussion
This loss function can deal with unbalanced classes and enhance segmentation accuracy and sensitivity. Parameter \(\beta_{l}=6\) was used to introduce a penalty for misclassified pixels, and also to avoid trapping the loss function in local minima. The \(\beta_{l}\) parameter can be in the range (0, 9). If the \(\beta_{l}\) parameter is set to a lower or higher value than 6, the results are worse overall, since the coefficient penalizes too few or too many false negatives. This approach set the final version of the method and led to the highest dice score, as well as increased precision. The fourth experiment examined the effects of varying percentages of ablations on the segmentation performance of the 3D V-Net architecture trained on the 3D-IRCADb dataset. As anticipated, the performance typically declined as the number of ablated filters increased. The results of the experiment showed that some filters contributed more to the performance than others. The effect of ablation is much stronger for stages ('down 64', 'up 64') for 50% ablation, and stages 'up 32' for 75% ablation. The stages ('down 256', 'up 256') showed negligible performance loss, as these stages may have redundant feature representation. This experiment determines the influence of different layers on the segmentation results. By identifying the network's sensitive areas, we want to modify the model's architecture in future research. In the last experiment, we compared the performance of the proposed method with some other algorithms described in the related work. We compared indirectly with [4,8,14,15,18] because it was not possible to re-implement or evaluate these methods with our dataset. A direct comparison was made with [16,23]. These methods were re-implemented based on the available source code and evaluated with our dataset. The authors in [23] just used a thresholding approach that did not provide satisfactory vessel segmentation. The authors of the paper [16] used 2.5D V-Net with a Tversky loss function, yielding results with high variability and a high mean standard deviation. We can state that our method prevails in dice score and specificity value over all of the other methods. In comparison with the two reimplemented methods, we dominate in terms of sensitivity, meaning that there are fewer missing and fragmented vessels. In terms of accuracy, our method lags just slightly behind. Overall, the method outlined in this paper can automatically segment enough liver vessels to correctly extract the vessels, and it can be further manually corrected using Slicer tools. The automatic segmentation results are visually presented in Section 7.5. The method works on CT obtained from different machines with different acquisition parameters. Ourmethod is also able to segment images with different liver shapes, livers with thinner vessels, and livers with vessels atrophied. The specific combination of the original image with the vessel-enhanced image is the originality of our work, and it outperforms other methods proposed in the literature. The method outlined in this paper have some limitations. The results in some cases have misclassified vessel endings, variability in vessel edges, and some regions are not connected to the main vessels. Finally, we need more data to make the model more robust, test the vessel segmentation, and lower the variability in our experiments.