We propose a novel pipeline for the generation of synthetic images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac ultrasound semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for medical image analysis tasks such as image segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiography images and trained a neural network for segmentation of the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.5 $\pm 6.0$ , 92.3 $\pm 3.9$, 86.3 $\pm 10.7$ \% for left ventricular endocardial, epicardial and left atrial segmentation respectively. This represents an increase of $9.09$, $3.7$ and $15.0$ \% in Dice scores compared to the previous state-of-the-art. The proposed pipeline has the potential for application to a wide range of other tasks across various medical imaging modalities.

翻译：我们提出了一种新型合成图像生成管线，该管线通过去噪扩散概率模型（DDPMs）并辅以心脏超声语义标签图进行引导。研究表明，这些合成图像可作为训练用于医学图像分析任务（如图像分割）的深度学习模型中真实数据的有效替代。为验证该方法的有效性，我们生成了合成二维超声心动图图像，并训练神经网络用于左心室和左心房分割。仅使用合成图像训练的网络在未见过的真实图像数据集上评估，其左心室内膜、外膜及左心房分割的平均Dice系数分别为88.5 $\pm 6.0$、92.3 $\pm 3.9$、86.3 $\pm 10.7$%。与先前最先进方法相比，Dice系数分别提升了$9.09$、$3.7$和$15.0$%。所提出的管线具有扩展应用于多种医学成像模态下广泛任务的潜力。