Simultaneous Localisation and Mapping (SLAM) of The Airways in Lung Bronchoscopy

肺支气管镜检查中气道的同步定位和绘图 (SLAM)

• 批准号: 2423383
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2423383
• 关键词: Simultaneous; Localisation; Mapping; SLAM; Airways

The proposed project aims to create a real-time 3D map of the lung airways, using feedback from an endoscopic camera located at the tip of a robotic bronchoscope, while simultaneously tracking the bronchoscope within the lung and localize it in the developed 3D map.The first stage of the research will focus on the localization and shape estimation of a robotic bronchoscope. The robot consists of several nested pre-curved thin tubes, which can be manipulated by rotation and translation, ultimately changing the overall shape of the robot. The goal is to obtain a method which could be used to accurately estimate the shape of the robot in real time. The methodology is based on Kalman filtering and sensor fusion, using a combination of an existing kinematic model of the robot and additional position sensors (e.g, CT scan images).The second stage aims to create a mapping of the bronchial tree. To achieve this, state-of-art computer vision and machine learning methods would be investigated, while making use of lung CT scans, real-life bronchoscopy video data and relevant image processing algorithms to segment the bronchial tree inside the lung. The focus would be on deep learning methods, since the traditional approaches such as feature matching have not performed well on the problems of similar nature.Having an accurate 3D mapping of the lungs, as well as localization of the robotic bronchoscope, would be a step closer towards autonomous navigation of the bronchoscope. The potential benefits of this are improved control and reduced human error, which in turn have been associated with higher success rate of the procedures, lower costs, and shortened recovery time for the patients.

该项目的目的是利用机器人支气管镜尖端的内窥镜摄像头的反馈创建肺气道的实时3D地图，同时跟踪肺内的支气管镜并将其定位在开发的3D地图中。研究的第一阶段将集中在机器人支气管镜的定位和形状估计上。该机器人由几个嵌套的预弯曲细管组成，可以通过旋转和平移来操纵，最终改变机器人的整体形状。目标是获得一种方法，可以用来准确地估计形状的机器人在真实的时间。该方法是基于卡尔曼滤波和传感器融合，使用现有的机器人和额外的位置传感器（例如，CT扫描图像）的运动模型的组合。第二阶段的目的是创建支气管树的映射。为了实现这一目标，将研究最先进的计算机视觉和机器学习方法，同时利用肺部CT扫描、真实支气管镜检查视频数据和相关图像处理算法来分割肺部内的支气管树。重点将放在深度学习方法上，因为传统的方法，如特征匹配，在类似性质的问题上表现不佳。拥有准确的肺部3D映射以及机器人支气管镜的定位，将更接近支气管镜的自主导航。这样做的潜在好处是改善控制和减少人为错误，这反过来又与更高的手术成功率、更低的成本和缩短患者的恢复时间有关。