The creation of image datasets for training deep neural networks mainly consists of data acquisition, data selection, and data labeling. Data acquisition is often limited, and data delivery is impaired by privacy regulations, especially in the medical imaging domain. Another major obstacle is the costly and time-intensive data labeling, which often requires medical professionals. Synthetic data may offer numerous benefits, including the ability to augment datasets with diverse and realistic images where real data is limited [1,2]. This reduces the costs and labor associated with annotating real images. Synthetic data also provides an ethical alternative to using sensitive patient data without compromising patient privacy or requiring ad hoc ethical committee approval for any specific project.