Institute of Biomedical Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, People's Republic of China.
Phys Med Biol. 2021 Jul 23;66(15). doi: 10.1088/1361-6560/abffef.
The uncertain motions of a target caused by the breath, heartbeat and body drift of a patient can increase the target locating error during image-guided interventions, and that may cause additional surgery trauma. A surgery navigation system with accurate motion tracking is important for improving the operation accuracy and reducing trauma. In this work, we propose an accurate and fast tracking algorithm in three-dimensional (3D) ultrasound (US) sequences for US-guided surgery to achieve moving object tracking. The idea of this algorithm is as follows. Firstly, feature pyramid architecture is introduced into a Siamese network to extract multiscale convolutional features. Secondly, to improve the network discriminative power and the robustness to ultrasonic noise and gain variation, we use the normalized cross correlation (NCC) to calculate the similarity between template block and search block. Thirdly, a fast NCC (FNCC) is proposed, which can perform the real-time tracking. Finally, a density peaks clustering approach is used to compensate the motion of the target and further improve the tracking accuracy. The proposed algorithm is evaluated on a CLUST dataset that includes 22 sets of 3D US sequences, and the mean error of 1.60±0.97 mm compared with manual annotations is obtained. After comparing with other published works, the results show that our algorithm achieves the comparable performance. The ablation study proves that the results benefit from the feature pyramid architecture and FNCC. These findings show that our algorithm may improve the motion tracking accuracy in image-guided interventions.
患者的呼吸、心跳和身体漂移会导致目标运动不确定，从而增加图像引导介入过程中的目标定位误差，这可能会导致额外的手术创伤。具有精确运动跟踪功能的手术导航系统对于提高手术精度和减少创伤至关重要。在这项工作中，我们提出了一种用于超声引导手术的三维（3D）超声（US）序列中的精确快速跟踪算法，以实现运动目标跟踪。该算法的思想如下。首先，将特征金字塔结构引入到 Siamese 网络中，以提取多尺度卷积特征。其次，为了提高网络的判别能力以及对超声噪声和增益变化的鲁棒性，我们使用归一化互相关（NCC）来计算模板块和搜索块之间的相似性。第三，提出了一种快速 NCC（FNCC），可以进行实时跟踪。最后，使用密度峰值聚类方法来补偿目标的运动，进一步提高跟踪精度。在包括 22 组 3D-US 序列的 CLUST 数据集上对所提出的算法进行了评估，与手动注释相比，平均误差为 1.60±0.97mm。与其他已发表的工作进行比较后，结果表明我们的算法具有可比的性能。消融研究证明，该结果受益于特征金字塔结构和 FNCC。这些发现表明，我们的算法可能会提高图像引导干预中的运动跟踪精度。
