## 3 Experimental Results
We also computed the Dice score of the total hematoma lesion segmentation with respect to the ground truth of the follow-up images, obtaining similar values for the three models (0.757, 0.763, and 0.761, respectively). When comparing our obtained results with the state-of-the-art approach presented in [14], we observed that our quantitative values are in a similar range, providing slightly better performance. For instance, our best mean Dice score of HE was 0.503 vs. 0.467 presented in [14]; we also obtained smaller AEV and AAEV and a global lesion Dice score of 0.763 (vs. 0.735). However, we want to emphasize that our dataset is much smaller than the one used in [14], which directly affects \begin{table}
\begin{tabular}{c c c c} \hline  & **Dice of Growth (\(\uparrow\))** & **AEV, mL (\(\leftrightarrow\))** & **AAEV, mL (\(\downarrow\))** \\ \hline Original images & 0.458 \(\pm\) 0.125 & \(-5.81\pm 11.54\) & 9.20 \(\pm\) 8.96 \\ Original images + DA & 0.481 \(\pm\) 0.118 & \(-5.09\pm 10.12\) & **8.15 \(\pm\) 7.77** \\ Original images + synthesis & **0.506 \(\pm\) 0.120 \(\ast\)** & \(-3.44\pm 11.87\) \(\ast\) & 8.49 \(\pm\) 8.85 \\ \hline [PERSON] et al. [14] & 0.467 & 4.80 & 10.50 \\ \hline \end{tabular} \(\ast\) Significant at \(p<0.05\).  
\end{table} 
Table 1: The average metrics across 27 leave-one-out experiments compared with the results of [14]. DA stands for traditional data augmentation. The values in bold specify the highest metric value. The significance of adding synthetic images to the training set is shown with \(\ast\) (\(\longleftarrow\)significant at \(p<0.05\)). this quantitative comparison. Nevertheless, we could achieve similar and sometimes better measures when using synthetic image generation and without requiring the inclusion of patient clinical data. In Figure 4a, we show the violin plots regarding the distribution of the Dice scores of the HE area for the different studied approaches. We can observe that the median Dice score of the growth area for the setting where we added synthetic images in the training phase is better than the ones of models trained only on original images and using traditional data augmentation. The same behavior can be seen for the interquartile range, which lies between higher values for the model exploiting the use of synthetic images. Furthermore, we noticed that for this setting, the distribution of the Dice scores of the HE area is more concentrated within the median value or higher, which also highlights the use of synthetic images. These results, together with the quantitative measures provided in Table 1, confirm that the addition of the synthetic data generated to improve the training process helps the model to learn HE more precisely, improving the predictions in most of the cases. Figure 4b shows the distribution of the prediction error in hematoma volume obtained with the proposed models. Again, we can see that both traditional data augmentation and image synthesis approaches reduce the error in the predicted volume, moving the median of the errors towards zero. Moreover, the distribution of the AEV obtained with the model trained using synthetic images has smaller interquartile ranges than the other two models, producing more robust and consistent results. A qualitative comparison among the different models is illustrated in Figure 5, where we show several examples with the baseline and follow-up CT images and the corresponding ground-truth masks, together with the predicted FU1 images and segmentation masks obtained with the three different models evaluated. The colors of the overlayed masks represent true-positive voxels, which are shown in red, false-positive voxels in green, and false-negative voxels in blue. From Figure 5a,b we see that the model trained with the addition of synthetic images better predicts the resulting hematoma masks, producing less false positives and false negatives.