In abdominal interventional therapy, accurate motion tracking of the target is regarded as crucial to minimize trauma and optimize dosage delivery. Meanwhile, three-dimensional (3D) ultrasound (US) is an attractive modality to show the real-time motion pattern of the target. In this work, we developed an accurate and robust landmark tracking algorithm for 3D US sequences. Methods: The proposed algorithm introduces a three orthogonal planes (TOPs) based scale discriminative correlation filter network for 3D US landmarks tracking. First, we couple the fully convolutional network (FCN) with scale discriminative correlation filter (SDCF) to generate an effective tracker. And SDCF is reformulated as a differentiable layer, which ensures the network can perform scale learning and end-to-end training. Next, we train the end-to-end network on millions of natural images. Finally, we convert 3D US image to 2D three-channel image by TOP transformation and feed them to the proposed network for performing online tracking. Results: Online tracking performance was evaluated on the Challenge of Liver Ultrasound Tracking (CLUST) dataset with 22 sets of 3D US sequences, obtaining mean error of 1.63 AE 1.04 mm and 95th percentile (95%ile) error of 3.37 mm, when compared with manual annotations annotated by surgeons. Ablation study indicates that the promising results benefit from SDCF and scale learning, which alleviates the influence from deformation. The findings of the clinical analysis support that the proposed algorithm can work well with different initial patch sizes, which means that our algorithm has potential to lighten the burden of surgeons. Conclusions: We propose a flexible, accurate and robust landmark tracking algorithm for the imageguided interventions, and our algorithm is comparable with the state-of-the-art approaches. The tracking accuracy and robustness show that our algorithm has potential in 3D US-guided abdominal interventional therapies. Furthermore, more researches are needed to improve the computing speed of the algorithm to achieve real-time tracking

Scene matching combined guidance is one of key effective approach to improve missile’s attacking precision. The paper introduces a scene matching combined guidance system. With the given reference image, the system can match the real-time images accurately through the improved self-learning mean removal normalized product correlation matching arithmetic. At last the paper presents the simulation result in the two typical movement patterns.