Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains. Existing UDA segmentation methods are not suitable for this task because they do not model local information and the cyclical consistency of heartbeat. In this paper, we introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for cardiac structure segmentation. Our GraphEcho comprises two innovative modules, the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle Consistency (TCC) module, which utilize prior knowledge of echocardiogram videos, i.e., consistent cardiac structure across patients and centers and the heartbeat cyclical consistency, respectively. These two modules can better align global and local features from source and target domains, improving UDA segmentation results. Experimental results showed that our GraphEcho outperforms existing state-of-the-art UDA segmentation methods. Our collected dataset and code will be publicly released upon acceptance. This work will lay a new and solid cornerstone for cardiac structure segmentation from echocardiogram videos. Code and dataset are available at: https://github.com/xmed-lab/GraphEcho

翻译：心动超声视频分割在心脏疾病诊断中扮演着重要角色。本文研究针对心动超声视频分割的无监督域适应问题，其目标是将源域训练的模型泛化至其他无标注的目标域。现有UDA分割方法不适用于此任务，因其未建模局部信息与心跳周期一致性。本文引入新采集的CardiacUDA数据集及创新的GraphEcho方法用于心脏结构分割。GraphEcho包含两个创新模块：空间交叉域图匹配模块与时间周期一致性模块，分别利用心动超声视频的先验知识——即跨患者与跨中心的心脏结构一致性及心跳周期一致性。这两个模块能更好地对齐源域与目标域的全局及局部特征，从而提升UDA分割结果。实验表明，本方法优于现有最先进的UDA分割方法。所采集数据集及代码将在接收后公开。本工作将为心动超声视频中的心脏结构分割奠定新的坚实基础。代码与数据集获取地址：https://github.com/xmed-lab/GraphEcho