Xinglong Zhu, Ruirui Kang, Yifan Wang, Danni Ai, Tianyu Fu and Jingfan Fan, Beijing Institute of Technology, China
Object tracking based on ultrasound image navigation can effectively reduce damage to healthy tissues in radiotherapy. In this study, we propose a deep Siamese network based on feature fusion. Whilst adopting MobileNetV2 as the backbone, an unsupervised training strategy is introduced to enrich the volume of the samples. The region proposal network module is designed to predict the location of the target, and a non-maximum suppression-based postprocessing algorithm is designed to refine the tracking results. Moreover, the proposed method is evaluated in the Challenge on Liver Ultrasound Tracking dataset and the self-collected dataset, which proves the need for the improvement and the effectiveness of the algorithm.
Ultrasound tracking, Siamese network, Respiratory motion estimation, One-shot learning.