使用2D VAE和GAN合成3D一致的心脏MR图像的病理学合成
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
AI总结：
本文提出利用2D VAE潜在空间插值进行标签变形，并结合标签条件GAN实现标签到图像转换，通过受试者内、受试者间和病理学三种合成策略生成3D一致的心脏MR图像，以增强多厂商多疾病数据下的分割泛化能力。
摘要：
我们提出了一种合成心脏磁共振（MR）图像的方法，该方法能够生成具有合理心脏病理和逼真外观的图像，目的是为监督深度学习（DL）训练的应用生成标注数据。图像合成包括标签变形和标签到图像的转换任务。前者通过VAE模型中的潜在空间插值实现，而后者通过标签条件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**，旨在插值一个受试者的中间切片以提高层间分辨率；ii）**受试者间合成**，旨在插值两个由不同扫描仪厂商采集的、不相似的受试者之间中间图像的几何形状和外观；iii）**病理学合成**，旨在合成一系列具有期望心脏病特征的伪病理合成受试者。此外，我们提出在重建之前对VAE潜在空间中2D切片之间的关系进行建模，以便通过堆叠逐层生成的2D切片来生成3D一致的受试者。我们证明，这种方法可以为多样化和丰富现有的心脏MR图像数据库提供解决方案，并为开发可泛化的基于DL的图像分析算法铺平道路。我们在数据增强场景中定量评估了合成数据的质量，以实现图像分割对多厂商和多疾病数据的泛化性和鲁棒性。我们的代码可在https://github.com/sinaamirrajab/CardiacPathologySynthesis获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
