Deep learning has gained significant attention in medical image segmentation.
However, the limited availability of annotated training data presents a
challenge to achieving accurate results. In efforts to overcome this challenge,
data augmentation techniques have been proposed. However, the majority of these
approaches primarily focus on image generation. For segmentation tasks,
providing both images and their corresponding target masks is crucial, and the
generation of diverse and realistic samples remains a complex task, especially
when working with limited training datasets. To this end, we propose a new
end-to-end hybrid architecture based on Hamiltonian Variational Autoencoders
(HVAE) and a discriminative regularization to improve the quality of generated
images. Our method provides an accuracte estimation of the joint distribution
of the images and masks, resulting in the generation of realistic medical
images with reduced artifacts and off-distribution instances. As generating 3D
volumes requires substantial time and memory, our architecture operates on a
slice-by-slice basis to segment 3D volumes, capitilizing on the richly
augmented dataset. Experiments conducted on two public datasets, BRATS (MRI
modality) and HECKTOR (PET modality), demonstrate the efficacy of our proposed
method on different medical imaging modalities with limited data.

通过提出基于 Hamiltonian 变分自动编码器（HVAE）和区别性正则化的新的端到端混合架构，我们的方法可以准确估计图像和蒙版的联合分布，从而生成具有较少伪影和离群实例的逼真医学图像。通过在丰富增强的数据集上以切片方式操作，我们的架构可以快速且准确地分割 3D 体积，在 BRATS（MRI 模态）和 HECKTOR（PET 模态）两个公开数据集上的实验证明了我们提出的方法在有限数据的不同医学成像模态上的有效性。