Correlation filters tissue tracking with application to robotic minimally invasive surgery

Purpose Vision-based tissue tracking is a significant component for building efficient autonomous surgical robot system. While the methodology involves various challenges caused by occlusion, deformation and appearance changes. Methods We propose a novel correlation filter tissue tracking framework for minimally invasive surgery. Our model contains the innovative design of synthetic features, a bi-branch is exploited to enhance the response map. An incrementally learned detector with the novel updating and trigger schemes is embedded to model the re-detection module for capturing the lost target. Results Promising validation has been conducted on the publicly available tracking benchmark datasets, a surgical tissue tracking dataset based on publicly available Cholec80 dataset has also been developed to focus on the application in intra-operative scenes. Conclusions Our proposed framework meets the outstanding performance and surpasses the existing methods. The work demonstrates the feasibility to perform tissue tracking by taking advantage of the correlation filter. This article is protected by copyright. All rights reserved.