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.

翻译：尽管深度学习在医学图像分割中的应用日益广泛，但获取充足的训练数据仍是医学领域面临的挑战。为此，数据增强技术应运而生，然而生成多样化且逼真的医学图像及其对应掩码仍是难题，尤其在训练集不足的情况下。为克服这些局限，我们提出了一种基于哈密顿变分自编码器（HVAE）的端到端架构。相较于传统变分自编码器（VAE），该方法改进了后验分布逼近效果，从而提升了图像生成质量。在数据稀缺条件下，本方法优于生成对抗网络架构，在图像质量和肿瘤掩码精确合成方面展现出显著改进。我们在两个公开数据集——MICCAI脑肿瘤分割挑战赛（BRATS）与头颈部肿瘤分割挑战赛（HECKTOR）上进行了实验，验证了该方法在不同医学成像模态下的有效性。