## 3 Experimental Results
Regarding the example shown in Figure 5c, we see that the model trained with the incorporation of synthetic images better perceives the pattern of growth of such elongated lesions, producing more growth along the longitudinal axis of the initial stroke lesion. From Figure 5d, we can see that none of the three models were able to predict the lesion growth perfectly, but in this particular case, the initial lesion presented quite an irregular shape. Of note, the predicted masks of the models trained on original images only and with traditional data augmentation techniques contain a vast number of false-positive voxels, and visually, they appear uniformly spread around the ground truth, which suggest that these models mostly learn to produce somewhat uniform growth, while  
Figure 4: Dice scores and hematoma volume prediction for the three different analyzed models. DA stands for traditional data augmentation. (**a**) Violin plots showing the distribution of Dice scores computed for the expansion area for different methods. (**b**) Boxplots showing the distribution of the prediction error in total hematoma volume for different methods. extending the training dataset with synthetic images helps the model to be more accurate in learning the possible HE patterns. To analyze the performance of our models in the best possible scenario, our approach relies on the spatial constraint provided by the ground truth of the hematoma mask in the  
Figure 5: Examples of the resulting masks obtained in our experiments. For each case (**a–d**), the top row shows (from left to right): baseline CT image, follow-up CT image, follow-up CT image overlayed with the resulting mask from the experiment performed only with original images, follow-up CT image overlayed with the resulting mask from the experiment performed with original images and traditional data augmentation, and follow-up CT image overlayed with the resulting mask from the experiment performed with original and synthetic images. The colors of the overlayed masks represent true-positive voxels (red), false-positive voxels (green), and false-negative voxels (blue). The bottom row for each case (**a–d**) shows the corresponding hematoma masks. DA stands for traditional data augmentation. initial CT scan. Therefore, the model does not have a bias due to the errors produced by the automated segmentation approach. Nevertheless, the current state-of-the-art approaches for hematoma segmentation [24; 31] achieve more than 0.85 in Dice score, making a good starting point for the prediction pipeline. To validate this, we performed an experiment where the leave-one-out cross-validation strategy was applied directly by using the automated segmentation mask provided by the segmentation approach [24]. The obtained results provided a Dice score of growth of 0.472 \(\pm\) 0.134 versus the 0.506 \(\pm\) 0.120 obtained when using the ground-truth masks. As expected, we noticed a slight drop in Dice score, but these results are still in line with our main findings and the ones of the state-of-the-art approach (Dice score of growth 0.467), proving that the initialization of the mask was already good enough not to affect the prediction results.