## 3 Experimental Results  
We carried out three experiments to (1) evaluate the performance of the model in the HE prediction task, (2) demonstrate the influence of adding synthetic images to the training set, and (3) analyze the use of standard data augmentation techniques (i.e., rotation and  
Figure 3: The 3D CNN architecture used in the proposed approach. The network consists of a 3D U-Net with skip connections and a spatial transformer network (STN). In the convolutional layers (Conv), \(\text{K}_{\text{x}}\text{K}_{\text{y}}\text{K}_{\text{z}}\text{@}\{\text{S}_{ \text{x}}\text{S}_{\text{y}}\text{,}\text{S}_{\text{z}}\}\) indicates the kernel and stride dimensions in each axis. The gray boxes represent the feature maps, with the number of channels indicated above them. \(L_{\text{seg}}\) is the segmentation loss computed between output and target masks; \(L_{\text{sim}}\) is the similarity loss between output and target images; and \(L_{\text{smooth}}\) restricts unrealistic displacement changes. flipping) on HE pattern prediction. In the baseline experiment, we trained the proposed network with all the steps and characteristics defined in Section 2.4.1 by using the original dataset, without the addition of synthetic images, and disabling the data augmentation performed during training. Then, we performed two more experiments, one extending the baseline experiment with traditional data augmentation and one adding the synthetic images into the training set. Three metrics were used to evaluate the performance of our predicted volumes: Dice similarity coefficient computed on the expanded area, average prediction error in hematoma volume (AEV), and absolute average error in hematoma volume (AAEV). To evaluate the statistical significance of the obtained results, we used the dependent \(t\)-test for paired samples. Due to the limited number of cases present in the dataset, we performed our experiments using a leave-one-out cross-validation strategy, meaning that every time, only one image was used for testing, while the remaining ones were used for the training and validation of the model. Notice that the proposed synthetic images were only included in the training set, and they were not used for testing. Thus, considering that we had 27 image pairs in our dataset, all the reported results shown in Table 1 are the average of the 27 performed experiments. Table 1 shows the average metrics obtained with the leave-one-out cross-validation when using only original images, original images with traditional data augmentation, and original images together with the synthetic ones. Note that in general, all the approaches effectively predicted HE with an average Dice score of growth area higher than 0.45. Further notice that even though the value might seem low, it is relevant, since in most of the cases, the increase was an area of small width compared with the global hematoma volume. From Table 1, we can see that the results of the model using only the original images provided the smallest Dice score, with a clear tendency to undersegment, i.e., generating less growth than expected. This is represented by the negative sign of the AEV value. Notice that this model also produced the highest ALEV, meaning that it was less accurate in predicting the final lesion volume. Additionally, it can be seen that in general, both data augmentation strategies were helpful for improving the obtained results, as expected in situations of scarce training data. The best results in terms of Dice score were obtained with the model that included the synthetic images in training, leading to better prediction of the area of HE and resulting in a nearly 5% Dice improvement with respect to the model trained only on the original data, with this being statistically significant (\(p<0.05\)), as well as producing a significantly smaller average prediction error in the final lesion volume (\(p<0.05\)).