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.

基于 Hamiltonian 变分自编码器（HVAE），提出了一种解决医学图像分割中训练数据不足挑战的端到端架构，通过改进传统变分自编码器（VAE）的后验分布近似，提高图像生成质量，在数据稀缺条件下优于生成对抗架构，实现了图像质量的优化和准确的肿瘤掩膜合成。在 MICCAI 的 Brain Tumor Segmentation Challenge（BRATS）和 Head and Neck Tumor Segmentation Challenge（HECKTOR）两个公开数据集上进行了实验，证明了我们方法在不同的医学成像模态上的有效性。