Label-Free Intraoperative Histological Imaging

这个项目是基于一种新的组织成像技术的科学开发，该技术将被用来最大限度地减少抽样误差，并提供更快和更准确的术中肿瘤边缘评估。计算显微镜和图像重建策略将被用于使用由斯特拉斯克莱德大学使用和开发的巨型物镜Mesolens进行高信息含量的无标记组织成像。通过将用于多模式照明的LED阵列与物理和深度学习策略相结合，特别是卷积神经网络(CNN)，将实现图像重建和分析的策略。该项目的目标是在5分钟内实现多毫米尺度组织活组织的高质量诊断成像。该项目将由斯特拉斯克莱德大学的盖尔·麦康奈尔教授、Michael Shaw博士和Catriona Douglas博士(NHS)监督。此外，还为该项目提供了许多关键领域的培训，如组织培养和细胞生物学、光学工程、生物医学成像和计算数据分析。

This project is based on the scientific development of a new histological imaging technique, which will be used to minimise the sampling errors and provide a faster and more accurate intraoperative assessment of the tumour margin. Computational microscopy and image reconstruction strategies will be used to enable high-information content, label-free histological imaging using the mesolens, a giant objective lens used and developed at the University of Strathclyde. By combing an array of LED's for multimodal illumination with physics and deep learning strategies, specifically, convolution neural networks (CNN), will allow strategies for image reconstruction and analysis.Our goal for the project is to enable high-quality diagnostic imaging of multi-mm scale tissue biopsies in under 5 minutes.The project will be supervised by Professor Gail McConnell (University of Strathclyde), Dr Michael Shaw (NPL) and Dr Catriona Douglas (NHS).Additionally, training is provided in many key areas for the project such as, tissue culture and cell biology, optical engineering, biomedical imaging, and computational data analysis.