Surgical data science for intelligent guidance and control in image-guided and robotic interventions

用于图像引导和机器人干预中智能引导和控制的手术数据科学

• 批准号: 2588156
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2588156
• 关键词: Surgical; data; science; intelligent; guidance

Research in surgical data science aims at improving the quality of interventional healthcare by leveraging information from heterogeneous data sources (e.g., medical imaging, sensors). By analysing these datasets, we will be able to model optimal surgical task execution and design skill-based controllers that will increase the autonomy of surgical robots resulting in more efficient surgeries with minimum invasiveness and trauma caused. This project will investigate hybrid control approaches incorporating both model-based and learning-based ones to provide increased manoeuvrability and dexterity in surgical robots. The model-based ones will ensure that safety constraints are satisfied, while the learning-based ones will navigate the various uncertainties during the procedure and increase the performance of the surgical task execution.Vision methods based on Deep Learning, would also be explored for localization and mapping of the environment using surgical cameras, offering intra-operative image guidance and enhancing clinical decision-making.The specific objectives are to:Model optimal surgical execution by fusing heterogeneous datasetsTranslate these models into control policies for surgical robots leading to increased automationCombine model-based and learning-based approaches to ensure that safety constraints are satisfiedImplement and evaluate intra-operative guidance/assistance systems to enhance surgical performanceAnalysing and building models from multimodal data (e.g., images, surgical videos, haptic and position sensors) can enhance the efficiency of surgical and robotic interventions. To model optimal surgical task execution, first, segmentation of the different actions during the procedure needs to be done, using tools from unsupervised learning. Then, each action can be modelled by fusing the aforementioned heterogeneous datasets. The resulting models can be used to develop novel solutions in surgical training and simulation, and also to increase robot autonomy by translating them into control policies (e.g., skill learning). However, the ability of the robot to perform the same skill effectively depends both on the quality of the data collected from skill demonstration, and the modelling approach itself. To improve the autonomous learning and imitation ability of robots, Reinforcement and Deep Learning are proposed, instead of traditional modelling methods. However, these powerful learning-based approaches need be combined with model-based control to ensure safe operation and high performance at the same time. Key EPSRC's research areas such as Medical Imaging, Artificial Intelligence Technologies, Robotics and Control Engineering are the project's core technical areas. The project is well-aligned with the current portfolio of EPSRC's research themes and specifically with Artificial Intelligence and Robotics and Healthcare Technologies. It also addresses two EPSRC Healthcare Technologies Grand Challenges in "Frontiers of Physical Intervention" and "Optimising Treatment" by focusing on data-driven methods and real-time analytics to realise new capabilities (autonomy, performance-based guidance) in surgical robotics. Project outcomes present opportunities for broad healthcare impact towards improving interventional outcomes with more accurate and safe procedures, increased access to surgery and personalising treatment, while reducing healthcare costs and patient recovery times.Opportunity for integration and further development of project outcomes in the surgical robotics industry is expected to arise during the project duration. Beneficiaries include manufacturers of commercial surgical robotics systems as well as developers of novel interventional robotic platforms.

手术数据科学的研究旨在通过利用来自异质数据源的信息（例如医学成像，传感器）来提高介入医疗保健的质量。通过分析这些数据集，我们将能够建模最佳手术任务执行和设计基于技能的控制器，从而增加手术机器人的自主权，从而导致更有效的手术，并以最小的侵入性和创伤。该项目将研究结合基于模型和基于学习的模型的混合控制方法，以提高手术机器人的机动性和灵活性。基于模型的模型将确保满足安全限制，而基于学习的限制将在过程中导航各种不确定性，并提高手术任务执行的性能。基于深度学习的VISION方法，也将探索用于使用手术摄像机的环境进行定位和映射，以使用手术摄像机进行策略，从而在内部的图像指导和增强特定的临床对象。将这些模型置于手术机器人的控制策略中，导致自动化机器人增加基于自动化模型和基于学习的方法，以确保安全限制满足安全性并评估术中的指导/援助系统，以增强外科手术性能和构建模型，从手术和机器人干预措施。为了建模最佳手术任务执行，首先，需要使用无监督学习的工具来完成过程中不同动作的分割。然后，可以通过融合上述异质数据集对每个动作进行建模。最终的模型可用于开发外科训练和模拟中的新颖解决方案，也可以通过将其转化为控制策略（例如，技能学习）来增加机器人自主权。但是，机器人有效地执行相同技能的能力既取决于从技能演示中收集的数据的质量，又取决于建模方法本身。为了提高机器人的自主学习和模仿能力，提出了加强和深度学习，而不是传统的建模方法。但是，这些强大的基于学习的方法需要与基于模型的控制相结合，以确保同时确保安全操作和高性能。 EPSRC关键的研究领域，例如医学成像，人工智能技术，机器人技术和控制工程，是该项目的核心技术领域。该项目与EPSRC研究主题的当前投资组合非常合并，特别是人工智能，机器人技术和医疗技术。它还通过专注于数据驱动的方法和实时分析，以实现手术机器人技术实现新能力（自主权，基于绩效的指导），从而解决了两种EPSRC Healthcare Technologies在“物理干预的边界”中遇到的巨大挑战和“优化治疗”。项目成果为通过更准确，更安全的程序，增加手术和个性化治疗而改善介入结果的广泛医疗保健影响有机会，同时降低医疗保健成本和患者康复时间。在项目持续时间内预计将出现整合和进一步发展外科机器人行业的项目成果。受益人包括商业外科机器人系统的制造商以及新型介入机器人平台的开发商。