Laboratory on Cardiovascular Imaging and Dynamics, KU Leuven, Belgium.
Laboratory on Cardiovascular Imaging and Dynamics, KU Leuven, Belgium.
Comput Med Imaging Graph. 2017 Dec;62:26-33. doi: 10.1016/j.compmedimag.2017.07.004. Epub 2017 Jul 29.
In this manuscript a novel method is presented for left ventricle (LV) tracking in three-dimensional ultrasound data using a hybrid approach combining segmentation and tracking-based clues. This is accomplished by coupling an affine motion model to an existing LV segmentation framework and introducing an energy term that penalizes the deviation to the affine motion estimated using a global Lucas-Kanade algorithm. The hybrid nature of the proposed solution can be seen as using the estimated affine motion to enhance the temporal coherence of the segmented surfaces, by enforcing the tracking of consistent patterns, while the underlying segmentation algorithm allows to locally refine the estimated global motion. The proposed method was tested on a dataset composed of 24 4D ultrasound sequences from both healthy volunteers and diseased patients. The proposed hybrid tracking platform offers a competitive solution for fast assessment of relevant LV volumetric indices, by combining the robustness of affine motion tracking with the low computational burden of the underlying segmentation algorithm.
在本文中，提出了一种新的方法，用于使用结合分割和基于跟踪线索的混合方法在三维超声数据中进行左心室 (LV) 跟踪。这是通过将仿射运动模型与现有的 LV 分割框架耦合，并引入一个能量项来实现的，该能量项惩罚使用全局 Lucas-Kanade 算法估计的仿射运动的偏差。所提出的解决方案的混合性质可以被视为利用估计的仿射运动来增强分割表面的时间一致性，通过强制跟踪一致的模式，而底层分割算法允许局部细化估计的全局运动。所提出的方法在由来自健康志愿者和患病患者的 24 个 4D 超声序列组成的数据集上进行了测试。所提出的混合跟踪平台通过将仿射运动跟踪的鲁棒性与底层分割算法的低计算负担相结合，为快速评估相关的 LV 容积指数提供了有竞争力的解决方案。
