Ultrasound is a promising medical imaging modality benefiting from low-cost and real-time acquisition. Accurate tracking of an anatomical landmark has been of high interest for various clinical workflows such as minimally invasive surgery and ultrasound-guided radiation therapy. However, tracking an anatomical landmark accurately in ultrasound video is very challenging, due to landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphism memory network (LSDM), which is a multi-task framework with an auxiliary learnable deformation prior to supporting accurate landmark tracking. Specifically, we design a novel diffeomorphic representation, which contains both long and short temporal information stored in separate memory banks for delineating motion margins and reducing cumulative errors. We further propose an expectation maximization memory alignment (EMMA) algorithm to iteratively optimize both the long and short deformation memory, updating the memory queue for mitigating local anatomical ambiguity. The proposed multi-task system can be trained in a weakly-supervised manner, which only requires few landmark annotations for tracking and zero annotation for deformation learning. We conduct extensive experiments on both public and private ultrasound landmark tracking datasets. Experimental results show that LSDM can achieve better or competitive landmark tracking performance with a strong generalization capability across different scanner types and different ultrasound modalities, compared with other state-of-the-art methods.

中文翻译：

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用于弱监督超声地标跟踪的长短微分同胚记忆网络

超声波是一种有前途的医学成像方式，受益于低成本和实时采集。准确跟踪解剖标志已引起微创手术和超声引导放射治疗等各种临床工作流程的高度关注。然而，由于标志变形、视觉模糊和部分观察，在超声视频中准确跟踪解剖标志非常具有挑战性。在本文中，我们提出了一种长短微分同态记忆网络（LSDM），它是一个多任务框架，在支持精确的地标跟踪之前具有辅助可学习变形。具体来说，我们设计了一种新颖的微分同胚表示，其中包含存储在单独的存储体中的长时态信息和短时态信息，用于描绘运动裕度并减少累积误差。我们进一步提出了一种期望最大化内存对齐（EMMA）算法来迭代优化长变形内存和短变形内存，更新内存队列以减轻局部解剖模糊性。所提出的多任务系统可以以弱监督的方式进行训练，只需要很少的地标注释用于跟踪和零注释用于变形学习。我们对公共和私人超声地标跟踪数据集进行了广泛的实验。实验结果表明，与其他最先进的方法相比，LSDM 可以在不同扫描仪类型和不同超声模式下具有强大的泛化能力，从而实现更好或有竞争力的地标跟踪性能。