  Despite the increasing use of deep learning in medical image segmentation,
acquiring sufficient training data remains a challenge in the medical field. In
response, data augmentation techniques have been proposed; however, the
generation of diverse and realistic medical images and their corresponding
masks remains a difficult task, especially when working with insufficient
training sets. To address these limitations, we present an end-to-end
architecture based on the Hamiltonian Variational Autoencoder (HVAE). This
approach yields an improved posterior distribution approximation compared to
traditional Variational Autoencoders (VAE), resulting in higher image
generation quality. Our method outperforms generative adversarial architectures
under data-scarce conditions, showcasing enhancements in image quality and
precise tumor mask synthesis. We conduct experiments on two publicly available
datasets, MICCAI's Brain Tumor Segmentation Challenge (BRATS), and Head and
Neck Tumor Segmentation Challenge (HECKTOR), demonstrating the effectiveness of
our method on different medical imaging modalities.
