Abstract
Ultrasound technology enables safe, non-invasive imaging of dynamic tissue behavior, making it a valuable tool in medicine, biomechanics, and sports science. However, accurately tracking tissue motion in B-mode ultrasound remains challenging due to speckle noise, low edge contrast, and out-of-plane movement. These challenges complicate the task of tracking anatomical landmarks over time, which is essential for quantifying tissue dynamics in many clinical and research applications. This manuscript introduces DUSTrack (Deep learning and optical flow-based toolkit for UltraSound Tracking), a semi-automated framework for tracking arbitrary points in B-mode ultrasound videos. We combine deep learning with optical flow to deliver high-quality and robust tracking across diverse anatomical structures and motion patterns. The toolkit includes a graphical user interface that streamlines the generation of high-quality training data and supports iterative model refinement. It also implements a novel optical-flow-based filtering technique that reduces high-frequency frame-to-frame noise while preserving rapid tissue motion. DUSTrack demonstrates superior accuracy compared to contemporary zero-shot point trackers and performs on par with specialized methods, establishing its potential as a general and foundational tool for clinical and biomechanical research. We demonstrate DUSTrack's versatility through three use cases: cardiac wall motion tracking in echocardiograms, muscle deformation analysis during reaching tasks, and fascicle tracking during ankle plantarflexion. As an open-source solution, DUSTrack offers a powerful, flexible framework for point tracking to quantify tissue motion from ultrasound videos. DUSTrack is available at this https URL.
Abstract (translated)
超声技术能够实现安全、无创的动态组织成像，因此在医学、生物力学和体育科学领域中具有重要价值。然而，在B模式超声图像中准确追踪组织运动仍然面临挑战，如斑点噪声（speckle noise）、低边缘对比度以及平面外移动等，这些因素使得随时间追踪解剖标志变得复杂，这对于许多临床和研究应用中的量化组织动力学至关重要。 本文介绍了一种基于深度学习和光流的超声跟踪工具包DUSTrack，这是一种半自动框架，用于在B模式超声视频中追踪任意点。我们结合了深度学习与光流技术，以实现跨不同解剖结构和运动模式的高质量、鲁棒性追踪。该工具包包括一个图形用户界面，可以简化高质量训练数据的生成，并支持迭代模型优化。此外，它还实施了一种新颖的基于光流的过滤技术，可以在减少帧间高频噪声的同时保留快速组织移动。 DUSTrack相较于当代零样本点跟踪器展现了更优的准确性，并且在性能上与专门的方法相当，证明了其作为临床和生物力学研究中通用而基础工具的潜力。我们通过三种应用场景展示了DUSTrack的多功能性：心脏壁运动追踪（使用心超图像）、伸手任务期间肌肉变形分析以及脚踝跖屈时肌纤维追踪。 作为一种开源解决方案，DUSTrack为从超声视频量化组织运动提供了强大且灵活的框架。更多关于DUSTrack的信息可以在提供的网址上获取。
URL
https://arxiv.org/abs/2507.14368
