基于扩散模型从舒张末期语义图合成超声心动图视频
Echocardiography video synthesis from end diastolic semantic map via diffusion model
AI总结：
本研究扩展视频扩散模型，利用舒张末期语义图合成超声心动图视频，并引入空间自适应归一化增强引导，在CAMUS数据集上多项指标优于标准扩散方法。
摘要：
去噪扩散概率模型（DDPMs）已在各种图像和视频生成任务中展现出显著成就，包括医学成像领域。然而，基于语义解剖信息生成超声心动图视频仍是一个尚未探索的研究领域。这主要是由于当前可用数据集的限制，这些数据集缺乏足够的规模以及针对每个心动周期的全面逐帧标注。本文旨在通过扩展现有视频扩散模型来解决上述挑战，以实现心脏视频合成。更具体地说，我们的重点在于利用心动周期中初始帧（通常称为舒张末期）的语义图来生成视频。为进一步改进合成过程，我们将空间自适应归一化集成到多尺度特征图中。这使得在合成过程中能够纳入语义引导，从而增强所得视频序列的真实感和连贯性。实验在CAMUS数据集上进行，该数据集是超声心动图领域广泛使用的数据集。在包括FID、FVD和SSMI在内的多项指标上，我们的模型相较于标准扩散技术表现出更优的性能。
英文摘要：
Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant achievements in various image and video generation tasks, including the domain of medical imaging. However, generating echocardiography videos based on semantic anatomical information remains an unexplored area of research. This is mostly due to the constraints imposed by the currently available datasets, which lack sufficient scale and comprehensive frame-wise annotations for every cardiac cycle. This paper aims to tackle the aforementioned challenges by expanding upon existing video diffusion models for the purpose of cardiac video synthesis. More specifically, our focus lies in generating video using semantic maps of the initial frame during the cardiac cycle, commonly referred to as end diastole. To further improve the synthesis process, we integrate spatial adaptive normalization into multiscale feature maps. This enables the inclusion of semantic guidance during synthesis, resulting in enhanced realism and coherence of the resultant video sequences. Experiments are conducted on the CAMUS dataset, which is a highly used dataset in the field of echocardiography. Our model exhibits better performance compared to the standard diffusion technique in terms of multiple metrics, including FID, FVD, and SSMI.
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