## 4 Discussion  
In this study, we have presented a patch-based deep learning approach for predicting the hemorrhagic stroke lesion area after its expansion. This problem is very challenging, considering that there is still no full understanding of the mechanisms and patterns of HE and that there are no publicly available large datasets with their corresponding annotations. Our proposal requires only CT imaging data with lesion annotations for training the models. We addressed the challenge of having limited annotated data of HE to perform the training of the models. To overcome this issue, we adapted the image synthesis proposed in [23] to generate more longitudinal training samples for hemorrhagic stroke lesions. This is a fully customizable and straightforward approach which does not require preliminary training. We analyzed the performance of different models: (1) training only on the original CT images, (2) training on the original images and the usage of standard data augmentation techniques, and (3) training on the original images with the addition of the generated synthetic images. All these models achieved results similar to those reported in the only related work available on the state of the art [14] but were obtained by using fewer data and without the need of adding non-imaging data. We observed that the model trained with the inclusion of the synthetic images produced overall better predictions of HE areas compared with the other analyzed models and the current results of the work of [14]. Notice that our deep learning approach is patch-based, meaning that we do not need to alter the original images; hence, we avoid losing important information regarding the hematoma growth pattern. Overall, with the leave-one-out evaluation performed, we obtained a mean Dice score for the growth area of 0.503, which is in line with the Dice score provided in the work of [14] (mean of 0.467). By analyzing the results per patient, we observed that the inclusion of image synthesis in the training process allowed us to improve the performance, obtaining higher Dice scores in 19 out of the 27 analyzed patients (this can be seen in Figure 4a), which was statistically significant with respect to the model trained only on original images (\(p<0.05\)). Furthermore, the model using the synthetic images could significantly reduce the average prediction error in hematoma volume (\(p<0.05\)), helping the model to be more robust and consistent in its predictions. In all our experiments, we observed overall an undersegmentation trend, as can be seen in the examples of Figure 5. This mostly happens in cases that have lesions with a high initial volume and a real growth value of more than 50%. Typically, the worst prediction results were obtained in cases with lesions that presented smaller average growth areas; therefore, small prediction errors directly affected the Dice score computation. From the obtained results, we observed that the incorporation of synthetic images was beneficial in cases with lesions of irregular shape or with irregular HE patterns. Even though their metrics may be lower than for more uniform lesions overall, the results were improved with the addition of synthetic data mainly due to the fact that we increased the representation of different lesions and HE growth patterns within the training dataset by doing so. Moreover, we observed that our approach tended to produce better predictions in cases with more uniformly shaped lesions, where augmenting the dataset using traditional data augmentation techniques or using the generated synthetic images helped to obtain better and more robust results. This study also has some limitations. The main one is the size of the available dataset that was used to train and evaluate our approaches. Our proposed model would indeed benefit from having more HE cases for the training process and also from having an independent testing dataset. However, in clinical practice, it is hard to collect a large number of cases with HE condition, having also the corresponding annotations.