Through automation, deep learning (DL) can enhance the analysis of transesophageal echocardiography (TEE) images. However, DL methods require large amounts of high-quality data to produce accurate results, which is difficult to satisfy. Data augmentation is commonly used to tackle this issue. In this work, we develop a pipeline to generate synthetic TEE images and corresponding semantic labels. The proposed data generation pipeline expands on an existing pipeline that generates synthetic transthoracic echocardiography images by transforming slices from anatomical models into synthetic images. We also demonstrate that such images can improve DL network performance through a left-ventricle semantic segmentation task. For the pipeline's unpaired image-to-image (I2I) translation section, we explore two generative methods: CycleGAN and contrastive unpaired translation. Next, we evaluate the synthetic images quantitatively using the Fr\'echet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher. In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic images. Furthermore, we compare established methods of assessing unpaired I2I translation and observe a disagreement when evaluating the synthetic images. Finally, we see which metric better predicts the generated data's efficacy when used for data augmentation.

翻译：通过自动化，深度学习（DL）能够增强对经食管超声心动图（TEE）图像的分析。然而，深度学习方法需要大量高质量数据才能产生准确结果，这一要求往往难以满足。数据增强是解决此问题的常用方法。在本研究中，我们开发了一个用于生成合成TEE图像及对应语义标签的流程。所提出的数据生成流程基于一个现有的合成经胸超声心动图图像生成流程进行扩展，通过将解剖模型的切片转换为合成图像。我们还通过左心室语义分割任务证明，此类图像能够提升深度学习网络的性能。针对流程中未配对图像到图像（I2I）转换部分，我们探索了两种生成方法：CycleGAN和对比式未配对翻译。接着，我们使用弗雷歇起始距离（FID）分数对合成图像进行定量评估，并通过一项涉及心脏病学专家和普通研究者的人类感知测试进行定性评估。在本研究中，当使用我们的合成图像增强数据集时，我们实现了高达10%的骰子系数提升。此外，我们比较了评估未配对I2I翻译的现有方法，并观察到在评估合成图像时存在分歧。最后，我们探究了哪种指标能更好地预测所生成数据用于数据增强时的效能。