State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China.
Int J Med Robot. 2022 Dec;18(6):e2440. doi: 10.1002/rcs.2440. Epub 2022 Aug 17.
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.
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 learnt detector with the novel updating and trigger schemes is embedded to model the re-detection module for capturing the lost target.
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.
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.
基于视觉的组织跟踪是构建高效自主手术机器人系统的重要组成部分。虽然该方法涉及到由于遮挡、变形和外观变化而引起的各种挑战。
我们提出了一种新的用于微创手术的相关滤波器组织跟踪框架。我们的模型包含了合成特征的创新设计，利用双分支来增强响应图。嵌入了一个递增学习的检测器，具有新颖的更新和触发方案，用于建模重新检测模块，以捕获丢失的目标。
在公开的跟踪基准数据集上进行了有前途的验证，还基于公开的 Cholec80 数据集开发了一个手术组织跟踪数据集，以专注于术中场景的应用。
我们提出的框架具有出色的性能，并超过了现有方法。这项工作证明了利用相关滤波器进行组织跟踪的可行性。
