Label-Free Intraoperative Histological Imaging

该项目基于一种新的组织学成像技术的科学发展，该技术将用于最大限度地减少采样误差，并提供更快，更准确的肿瘤边缘术中评估。计算显微镜和图像重建策略将用于实现高信息含量，无标记的组织成像使用mesolens，一个巨大的目标透镜使用和开发的斯特拉斯克莱德大学。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），将允许图像重建和分析策略。我们的目标是在5分钟内实现多mm尺度组织活检的高质量诊断成像。该项目将由Gail McConnell教授监督（斯特拉斯克莱德大学），Michael Shaw博士（NPL）和Catriona道格拉斯博士（NHS）。此外，该项目还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.