## 5 Datasets and Improved Annotations
### Dataset 3D-IRCADb  
The proposed method has been evaluated on the publicly available 3D-IRCADb [2] dataset. We have specifically used its subset 3D-IRCADb-01, which is used in most of the similar research, and thus allows for the comparison. The dataset contains 20 CT volumes  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Name of dataset** & **Published** & **Number of patients** \\ \hline
3D-IRCADb [2] & 5 May 2019 & 22 patients \\ \hline Medical Segmentation Decathlon (MSD) [30] & 20 December 2020 & 430 patients \\ \hline Vascular Synthesizer (VascuSynth) [24] & March 2013 & 120 synthetic samples \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of publicly available datasets.  
Figure 2: Slicer’s Segment Editor with the AIAA extension.  
(10 male and 10 female). Fifteen cases (75%) of this dataset have hepatic tumors. The dataset contains various classes. The classes used in our study are portalvein and venoussystem. We combined them into one class of veins. The pixel spacing varies from 0.56 to 0.84 mm, and the slice thickness varies from 1 to 4 mm.  
There are several challenges within the dataset. In some cases, the inferior vena cava is not part of the liver mask. Besides that, one patient had many liver tumors that covered the majority of the liver vessels. Another patient had inner metallic objects, such as stents, that significantly affected the brightness of the volume. Despite these bottlenecks, we kept all of the volumes from the dataset in the experiment to have more training data. Furthermore, slight abnormalities make the network more robust, even if the output quality suffers. Although low-quality data were used, our technique was still able to achieve a high prediction quality. The training and testing sets should equally include cases of liver vessels of similar appearance (both containing difficult and easy cases). To ensure this, we split the dataset as follows. _Training set_ contains patients--2, 5, 7, 12, 10, 11, 13, 16, 18, 19. _Validation_ includes patients--4, 9, 20, 6, 17. _Testing set_ is represented by patients--1, 3, 8, 14, 15.  
Improving Annotations of 3D-IRCADb  
Since there are some limitations in the annotations of hepatic vessels in the 3D-IRCADb dataset, we decided to improve them. The limitations are mainly because of inadequately annotated hepatic veins, although they are clearly visible and could be marked correctly. Similar problems have been found and tackled by [8; 31]. Some of the CT volumes in 3D-IRCADb are under-segmented, while others are over-segmented. These issues might lead to the misinterpretation of results when segmented vessels appear as false positives or false negatives while testing the neural networks [18]. Another major limitation is the inconsistency of the annotated data for the vena cava. The vena cava is visible in some images but not in others. It also makes vessel enhancement difficult [8].  
Because the annotated datasets used in [8; 31] are not publicly available, the additional labeling of the missing veins in 3D-IRCADb has been performed using the Slicer built-in tools and the extension explained in Section 3. It increased the quality of annotations; see Figure 3. The comparison of the dice scores with the original and improved dataset is in the experimental part of the paper. If it is not stated differently, the experiments used refined annotations since the vessel prediction is better and more continuous with them.