'Intra-operative probe design and image processing optimisation with deep learning for in-vivo and ex-vivo detection of cancerous tissue'

“通过深度学习进行术中探头设计和图像处理优化，用于体内和离体癌组织检测”

• 批准号: 2272219
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2272219
• 关键词: Intra; operative; probe; design; image

This project involves innovative methodologies, advanced physics modelling and deep-learned image and signal processing to advance the state-of-the-art in intra-operative probes for cancer surgery.This research project will build both a theoretical and practical understanding of two key and complementary approaches: 1) in-vivo detection of cancer during laparoscopic surgery and 2) ex-vivo detection of cancerous margins on samples. This will likely involve Monte Carlo simulations of the radiation physics (e.g. GEANT4) and the probe, investigating sources of interference, as well as developing deep-learning assisted image and signal processing techniques (e.g. in MATLAB/Python) in order to exploit all measured data to its fullest informative extent. It is anticipated that deep learning will greatly assist in the signal and image processing, allowing enhanced discrimination between electrons and photons compared to analytic or hand-crafted / intuitive methods for signal discrimination.If the project starts to consider imaging, then use of high quality training dataset pairs (low count and high count data) with deep learning will allow powerful noise reduction, and even the possibility of enhanced spatial resolution, through use of generative modelling.The project has scope to extend all the way from probe configuration optimisation through to development of AI-assisted real-time surgical imaging capabilities.Probe design optimisation will involve experimental and analysis work to understand and evaluate methods and detectors for the detection and identification of internal conversion electrons from 99mTc. This process is attractive as 99mTc is the most widely used radionuclide for nuclear medicine studies, and so complex regulatory issues can be avoided. Reliable detection, identification and localisation these electrons poses many challenges associated with low internal conversion (IC) electron yield, electron energy loss in tissue, and gamma and electron background (in addition to the constraints of laparoscopy). These issues will be characterised experimentally and with reference to appropriate models, initially to obtain a good understanding of the processes and trade-offs involved and subsequently in the context of workable probe systems. Experiments will be performed initially in the context of an existing Lightpoint prototype device that uses a CMOS sensor to directly image the IC electrons. However other detectors types in various configurations may also be feasible and will be investigated and evaluated

该项目涉及创新的方法，先进的物理建模和深入学习的图像和信号处理，以推进最先进的术中探针用于癌症手术。该研究项目将建立两个关键和互补的方法的理论和实践理解：1）腹腔镜手术中的癌症体内检测和2）样本癌边缘的体外检测。这可能涉及辐射物理（例如GEANT 4）和探针的蒙特卡罗模拟，调查干扰源，以及开发深度学习辅助图像和信号处理技术（例如在MATLAB/Python中），以便充分利用所有测量数据。预计深度学习将极大地帮助信号和图像处理，与用于信号辨别的分析或手工制作/直观方法相比，可以增强电子和光子之间的辨别力。如果项目开始考虑成像，则使用高质量的训练数据集对（低计数和高计数数据）与深度学习的结合将允许强大的降噪，甚至可能增强空间分辨率，通过使用生成建模。该项目的范围从探头配置优化一直延伸到人工智能辅助的真实的开发，探针设计优化将涉及实验和分析工作，以了解和评估用于检测和识别的方法和检测器99 mTc的内转换电子。这个过程很有吸引力，因为99 mTc是核医学研究中使用最广泛的放射性核素，因此可以避免复杂的监管问题。可靠地检测、识别和定位这些电子带来了许多挑战，这些挑战与低内部转换（IC）电子产率、组织中的电子能量损失以及伽马和电子背景（除了腹腔镜检查的限制之外）相关。这些问题的特点是实验和参考适当的模型，最初获得一个很好的理解的过程和权衡所涉及的，随后在可行的探针系统的上下文中。实验最初将在现有的Lightpoint原型设备的背景下进行，该设备使用CMOS传感器直接对IC电子进行成像。然而，各种配置的其他探测器类型也是可行的，将对其进行调查和评估