We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data for the application of supervised deep-learning (DL) training. The image synthesis consists of label deformation and label-to-image translation tasks. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a label-conditional GAN model. We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease. Furthermore, we propose to model the relationship between 2D slices in the latent space of the VAE prior to reconstruction for generating 3D-consistent subjects from stacking up 2D slice-by-slice generations. We demonstrate that such an approach could provide a solution to diversify and enrich an available database of cardiac MR images and to pave the way for the development of generalizable DL-based image analysis algorithms. We quantitatively evaluate the quality of the synthesized data in an augmentation scenario to achieve generalization and robustness to multi-vendor and multi-disease data for image segmentation. Our code is available at https://github.com/sinaamirrajab/CardiacPathologySynthesis.

翻译：我们提出一种合成具有合理心脏病理形态和真实外观的心脏磁共振（MR）图像的方法，旨在为监督式深度学习（DL）训练生成标注数据。图像合成包含标签形变和标签到图像转换两项任务：前者通过VAE模型中的潜在空间插值实现，后者通过标签条件GAN模型完成。我们设计了三种在训练好的VAE模型潜在空间中进行标签操作的方法：i) **受试者内合成**——通过插值同一受试者的中间切片以提高面内分辨率；ii) **受试者间合成**——在两个不同扫描仪厂商采集的非相似受试者之间插值中间图像的几何形态与外观；iii) **病理合成**——生成一系列具有预期心脏病特征的伪病理合成受试者。此外，我们在重建前对VAE潜在空间中二维切片间的关系进行建模，通过逐层堆叠二维切片生成三维一致性受试者。实验证明，该方法能够为现有心脏MR图像数据库的多样化与丰富化提供解决方案，并为开发可泛化的基于DL的图像分析算法铺平道路。在数据增强场景下，我们通过定量评估合成数据的质量，实现了对多厂商、多疾病数据的泛化性与鲁棒性的图像分割效果。代码开源地址为https://github.com/sinaamirrajab/CardiacPathologySynthesis。