论文标题
基于超分辨率的机器人辅助微创手术的实时手术环境增强
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution
论文作者
论文摘要
在机器人辅助的微创手术（RAMIS）中，通常需要摄像机助手来控制腹腔镜的位置和缩放率，按照外科医生的说明。但是，经常移动腹腔镜可能会导致不稳定和次优的视图，而缩小比的调整可能会中断手术操作的工作流程。为此，我们提出了一个基于多尺度的生成对抗网络（GAN）的视频超分辨率方法，以构建一个自动缩放比率调整的框架。它可以提供自动实时缩放，以在手术操作过程中高质量地可视化感兴趣区域（ROI）。在框架的管道中，核心相关滤波器（KCF）跟踪器用于跟踪手术工具的尖端，而基于半全球块匹配（SGBM）的深度估计（SGBM）的深度估计和复发性神经网络（RNN）的上下文意识是开发出来的，以确定升级率的升级率。该框架通过拼图数据集和Hamlyn Center腹腔镜/内窥镜视频数据集进行了验证，结果证明了其实用性。
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-quality visualization of the Region Of Interest (ROI) during the surgical operation. In the pipeline of the framework, the Kernel Correlation Filter (KCF) tracker is used for tracking the tips of the surgical tools, while the Semi-Global Block Matching (SGBM) based depth estimation and Recurrent Neural Network (RNN)-based context-awareness are developed to determine the upscaling ratio for zooming. The framework is validated with the JIGSAW dataset and Hamlyn Centre Laparoscopic/Endoscopic Video Datasets, with results demonstrating its practicability.
