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构数据源（例如，医学成像、传感器）。通过分析这些数据集，我们将能够对最佳手术任务执行进行建模，并设计基于技能的控制器，这将提高手术机器人的自主性，从而以最小的侵入性和创伤实现更高效的手术。该项目将研究混合控制方法，结合基于模型和基于学习的方法，以提高手术机器人的机动性和灵活性。基于模型的方法将确保满足安全约束，而基于学习的方法将在手术过程中导航各种不确定性，并提高手术任务执行的性能。基于深度学习的视觉方法也将被探索用于使用手术摄像机定位和绘制环境，提供术中图像指导并增强临床决策。具体目标是：通过融合异构机器人对最佳手术执行进行建模将这些模型转换为手术机器人的控制策略，从而提高自动化程度结合基于模型和基于学习的方法，以确保满足安全约束实施和评估术中指导/辅助系统，以提高手术性能从多模态数据（例如，图像、手术视频、触觉和位置传感器）可以提高手术和机器人干预的效率。为了对最佳手术任务执行进行建模，首先，需要使用来自无监督学习的工具对手术期间的不同动作进行分割。然后，每个动作可以通过融合上述异构数据集来建模。由此产生的模型可以用于开发手术训练和模拟中的新解决方案，并且还可以通过将它们转化为控制策略来增加机器人自主性（例如，技能学习）。然而，机器人有效地执行相同技能的能力取决于从技能演示中收集的数据的质量和建模方法本身。为了提高机器人的自主学习和模仿能力，提出了强化和深度学习，而不是传统的建模方法。然而，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，以确保安全运行和高性能的同时。EPSRC的关键研究领域，如医学成像，人工智能技术，机器人和控制工程是该项目的核心技术领域。该项目与EPSRC当前的研究主题组合非常一致，特别是与人工智能、机器人和医疗保健技术。它还通过专注于数据驱动的方法和实时分析来解决EPSRC医疗保健技术在“物理干预前沿”和“优化治疗”方面的两个重大挑战，以实现手术机器人的新功能（自主性，基于性能的指导）。项目成果为改善介入治疗结果提供了广泛的医疗影响机会，包括更准确和安全的手术，增加手术和个性化治疗的机会，同时降低医疗成本和患者恢复时间。在手术机器人行业中，项目成果的整合和进一步发展的机会预计将在项目期间出现。受益者包括商业手术机器人系统的制造商以及新型介入机器人平台的开发商。