Segmentation of the left atrial (LA) wall and endocardium from late gadolinium-enhanced (LGE) MRI is essential for quantifying atrial fibrosis in patients with atrial fibrillation. The development of accurate machine learning-based segmentation models remains challenging due to the limited availability of data and the complexity of anatomical structures. In this work, we investigate 3D conditional generative models as potential solution for augmenting scarce LGE training data and improving LA segmentation performance. We develop a pipeline to synthesize high-fidelity 3D LGE MRI volumes from composite semantic label maps combining anatomical expert annotations with unsupervised tissue clusters, using three 3D conditional generators (Pix2Pix GAN, SPADE-GAN, and SPADE-LDM). The synthetic images are evaluated for realism and their impact on downstream LA segmentation. SPADE-LDM generates the most realistic and structurally accurate images, achieving an FID of 4.063 and surpassing GAN models, which have FIDs of 40.821 and 7.652 for Pix2Pix and SPADE-GAN, respectively. When augmented with synthetic LGE images, the Dice score for LA cavity segmentation with a 3D U-Net model improved from 0.908 to 0.936, showing a statistically significant improvement (p < 0.05) over the baseline.These findings demonstrate the potential of label-conditioned 3D synthesis to enhance the segmentation of under-represented cardiac structures.

翻译：从晚期钆增强（LGE）磁共振成像中分割左心房（LA）壁和心内膜对于量化心房颤动患者的心房纤维化至关重要。由于数据可用性有限以及解剖结构的复杂性，开发基于机器学习的精确分割模型仍然具有挑战性。在本工作中，我们研究了三维条件生成模型作为增强稀缺LGE训练数据并提升LA分割性能的潜在解决方案。我们开发了一个流程，利用三种三维条件生成器（Pix2Pix GAN、SPADE-GAN和SPADE-LDM），从结合解剖学专家标注与无监督组织聚类的复合语义标签图中合成高保真度的三维LGE MRI体数据。我们对合成图像的逼真度及其对下游LA分割任务的影响进行了评估。SPADE-LDM生成了最逼真且结构最准确的图像，其FID得分为4.063，超越了GAN模型（Pix2Pix和SPADE-GAN的FID分别为40.821和7.652）。当使用合成LGE图像进行数据增强时，采用三维U-Net模型的LA腔分割Dice分数从0.908提升至0.936，相比基线显示出统计学上的显著改善（p < 0.05）。这些发现证明了基于标签条件的三维合成在增强代表性不足的心脏结构分割方面的潜力。