LSDM：用于弱监督超声界标跟踪的长短同构运动
LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking
摘要：
随着时间的推移对解剖标志的准确跟踪对于诸如微创手术和肿瘤放射治疗的疾病评估具有高度的兴趣。超声成像是一种很有前途的模式，受益于低成本和实时采集。然而，生成精确的地标轨迹是非常具有挑战性的，因为尝试可以容易地被不同的干扰（例如地标变形、视觉模糊和部分观察）扭曲。在本文中，我们提出了一个长短同构运动网络，这是一个多任务的框架，具有可学习的变形之前，寻找合理的变形的地标。具体来说，我们设计了一种新的双态表示在长和短的时间域划定运动的利润和减少长期累积跟踪误差。为了进一步减轻局部解剖模糊性，我们提出了一个期望最大化运动对齐模块，以迭代优化长和短变形，对齐到相同的方向和空间表示。所提出的多任务系统可以以弱监督的方式进行训练，这只需要很少的地标标注用于跟踪，并且对于长短变形学习，零标注。我们在两个超声标志跟踪数据集上进行了广泛的实验。实验结果表明，我们提出的方法可以实现更好的或有竞争力的地标跟踪性能相比，其他国家的最先进的跟踪方法，具有较强的泛化能力，在不同的扫描仪类型和不同的超声模态。
英文摘要：
Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. Ultrasound imaging is a promising modality benefiting from low-cost and real-time acquisition. However, generating a precise landmark tracklet is very challenging, as attempts can be easily distorted by different interference such as landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prior to search for the plausible deformation of landmark. Specifically, we design a novel diffeomorphism representation in both long and short temporal domains for delineating motion margins and reducing long-term cumulative tracking errors. To further mitigate local anatomical ambiguity, we propose an expectation maximisation motion alignment module to iteratively optimize both long and short deformation, aligning to the same directional and spatial representation. 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 long-short deformation learning. We conduct extensive experiments on two ultrasound landmark tracking datasets. Experimental results show that our proposed method can achieve better or competitive landmark tracking performance compared with other state-of-the-art tracking methods, with a strong generalization capability across different scanner types and different ultrasound modalities.
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