文章基本信息

标题：Feature Fusion-Based Siamese Region Proposal Network for Ultrasound Tracking
本地全文：下载
作者：Xinglong Zhu ; Ruirui Kang ; Yifan Wang 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：15
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
关键词：Ultrasound tracking;Siamese network;Respiratory motion estimation;One-shot learning