Multi-modal fusion improves long-term prediction of microsurgery tool movements
Multi-modal Representation Fusion for Long-term Microsurgical Anastomosis Trajectory Anticipation
RoboticsComputer Vision and Pattern Recognition
Summary
Predicting how surgical tools will move during very delicate microsurgery is hard but important for robotic assistance. The authors created a new system that combines different types of information—like what objects are in the view and how they move—to guess tool paths in both 2D and 3D for a longer time ahead. Their approach uses a special process to mix these clues over time, making the predictions more accurate and robust across different surgical setups. This could help robots assist surgeons better or even perform parts of the surgery more autonomously.
What this means in practice
- •For robotic surgery engineers: Improve robotic systems to predict instrument movements in microsurgery for better assistance and planning during operations.
- •For medical device developers: Develop advanced surgical robots capable of anticipating tool trajectories to support autonomous or semi-autonomous procedures.$Commercial implications: Enables creation of intelligent surgical robots with predictive capabilities, enhancing precision and safety in clinical microsurgery.
Authors
Yuxuan Liu, Yating Luo, Yunfei Luan, Xinyao Zhou, Yuyang Zhuge, Yao Guo, Guang-Zhong Yang
Abstract
Microsurgical anastomosis is one of the most challenging and sophisticated procedures for surgical robotics, requiring accurate manipulation of surgical instruments within a limited space to achieve precise operations. Accurate surgical trajectory anticipation is essential for surgical scene understanding, intraoperative planning, robotic manipulation, and reliable behaviour analysis. However, existing approaches mainly focus on features extracted from surgical videos and historical trajectories, while overlooking rich semantic and motion information that can provide more discriminative multi-modal representations for complex surgical instrument-tissue interactions. In this work, we propose a cross-modal feature fusion framework for long-term 2D and 3D microsurgical anastomosis trajectory anticipation. The proposed framework consists of a multi-modal representation extractor and a cross-modal feature fusion network. Semantic recognition of surgical objects and consecutive motion estimation are incorporated as multi-modal visual cues, providing complementary spatial and temporal guidance for instrument-tissue interaction characterization. A coarse-to-fine progressive fusion strategy with a long-short-term memory mechanism is introduced to capture temporal dependencies step by step. The training and evaluation are conducted on our established robotic platform and a comprehensive dataset containing both phantom and ex-vivo surgical scenarios is constructed. Extensive quantitative and qualitative experiments demonstrate that the proposed framework consistently outperforms existing methods and exhibits improved robustness across different surgical scenes, highlighting its potential for both intelligent robot-assisted and autonomous surgical systems.